Instruction: I am organizing the research trajectory of dataset distillation and dataset condensation on arXiv from 2019 to 2021, aiming to identify machine learning papers that discuss using a small set of synthetic training data to replace large-scale training sets. From the 2019–2021 arXiv records, retain only entries whose primary category is Machine Learning; if a paper is only cross-listed into Machine Learning by another primary category, do not include it. Additionally, these papers must have a revision within the next calendar year after the initial submission, and the current record must still clearly indicate a subsequent publication venue. Output a table sorted by v1 submitted in ascending order, with columns: arXiv ID, term family, cue location, v1 submitted, first next-year version, date of that version, primary category, publication-trail source, current publication clue. If no qualifying entries exist, output NONE.

arXiv ID|term family|cue location|v1 submitted|first next-year version|date of that version|primary category|publication-trail source|current publication clue
1910.02551|Distillation|Title|2019-10-06|v3|2020-05-05|cs.LG|Related DOI|10.1109/IJCNN52387.2021.9533769
2011.00050|Distillation|Abstract-only|2020-10-30|v2|2021-03-02|cs.LG|Comments|Accepted to ICLR 2021
2107.13034|Distillation|Title|2021-07-27|v3|2022-01-17|cs.LG|Comments|NeurIPS 2021
2110.04181|Condensation|Title|2021-10-08|v2|2022-04-21|cs.LG|Journal reference|Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2023 (WACV)

Only include arXiv records where v1 submitted falls between 2019-01-01 and 2021-12-31.

The title or abstract must explicitly use terms from the dataset distillation or dataset condensation family, and the semantics must refer to replacing large-scale training sets with a small set of synthetic training data.

The current primary category must be cs.LG; if a paper is only cross-listed into Machine Learning by another primary category, do not include it.

The paper must have a revision within the next calendar year after the initial submission, and the current record must still clearly indicate a subsequent publication venue.

Each candidate abstract page yields primary category, v1 date, first next-year version, cue location, and publication-trail source

Answer rows are produced only after deduplication and detail-page validation

Records must be filtered by original submission year, with per-record validation that a version in the next calendar year exists

Cross-listed-only hits must be excluded, and the current primary category must be validated on abstract pages

Searches must cover both Title and Abstract because valid distillation hits can appear only in the abstract

Topic matches without a current publication trail still fail until the Comments, Journal reference, or Related DOI field is checked on the abstract page

Exclude records whose current primary category is not cs.LG and that are only cross-listed to cs.LG.

Exclude records where the title or abstract contains distillation/condensation terms but does not correspond to the dataset distillation or dataset condensation research line.

Exclude records that have no version revision in the next calendar year, or whose current record contains no clearly visible publication clue.

required literal first row shown in the oracle

arXiv ID
term family
cue location
v1 submitted
first next-year version
date of that version
primary category
publication-trail source
current publication clue

if both families appear, prefer the title hit; if both appear only in the abstract, keep the first abstract occurrence

qualifying cue appears only in the title

qualifying cue appears only in the abstract

qualifying cue appears in both title and abstract, or title carries only a generic cue while the abstract carries the specific family name

keep the shortest phrase that still preserves venue or publication-status information; when sourced from Related DOI, drop the DOI prefix and keep only the informative clue

all four corrected result pages are exhausted

all unique candidate IDs are opened to abstract-page depth

for every surviving candidate, the abstract page, current browse context, and publication-trail fields are checked before emitting the final ordered table


Instruction: I am compiling a reading list of 2022 research on reasoning prompting for large language models. From arXiv papers whose first version (v1) was submitted in 2022, I want to select those whose title or abstract explicitly focuses on chain-of-thought or self-consistency reasoning and whose primary category is Computation and Language. Candidate papers must also have a version update in 2023, and the current arXiv record's Comments field must directly indicate a subsequent publication venue. Output a table sorted by the date of each paper's earliest 2023 version in ascending order, with columns: arXiv ID, cue family, cue location, v1 submitted, first 2023 version, date of that version, primary category, publication clue. If no qualifying papers exist, output NONE.

arXiv ID|cue family|cue location|v1 submitted|first 2023 version|date of that version|primary category|publication clue
2210.01240|Chain-of-thought|Title + Abstract|2022-10-03|v2|2023-01-25|cs.CL|Published as a conference paper at ICLR 2023
2205.11916|Chain-of-thought|Abstract-only|2022-05-24|v4|2023-01-29|cs.CL|Accepted to NeurIPS2022
2203.11171|Self-consistency; Chain-of-thought|Title + Abstract|2022-03-21|v4|2023-03-07|cs.CL|Published at ICLR 2023; camera ready version at ICLR 2023
2210.03629|Chain-of-thought|Abstract-only|2022-10-06|v3|2023-03-10|cs.CL|v3 is the ICLR camera ready version
2210.03350|Chain-of-thought|Abstract-only|2022-10-07|v2|2023-05-23|cs.CL|To appear at Findings of EMNLP 2023
2212.10001|Chain-of-thought|Title + Abstract|2022-12-20|v2|2023-06-01|cs.CL|ACL-23 Camera Ready
2212.08061|Chain-of-thought|Abstract-only|2022-12-15|v2|2023-06-04|cs.CL|ACL 2023 Main Conference
2212.10071|Chain-of-thought|Abstract-only|2022-12-20|v2|2023-06-13|cs.CL|ACL 2023 camera-ready
2212.10509|Chain-of-thought|Title + Abstract|2022-12-20|v2|2023-06-23|cs.CL|ACL'23 Camera Ready
2211.12588|Chain-of-thought; Self-consistency|Abstract-only|2022-11-22|v4|2023-10-23|cs.CL|Published at TMLR 2023

Only retain arXiv papers whose v1 was submitted in 2022.

The title or abstract must explicitly contain cues related to chain-of-thought or self-consistency.

The current primary category must be cs.CL; do not include records that are only cross-listed from other primary categories.

The paper must have a version update in 2023, and the current Comments field must clearly indicate a subsequent publication venue.

each candidate abstract page yields primary category, v1 date, first 2023 version, comments/publication clue

answer rows are produced only after deduplication and detail-page validation

searches must cover both Title and Abstract fields because valid rows can appear only in the abstract

cross-listed hits whose primary category is not cs.CL must be excluded, and the primary category must be validated on the abstract page

topic-matched hits must be validated against a current Comments publication clue before they can be accepted

2023 candidates must be identified by original submission year and full version-history checks, rather than by latest-date proxies

v1 submitted in 2022; version-history event in 2023

Exclude records whose current primary category is not cs.CL, even if they appear in relevant search results.

Exclude records that contain only vague abbreviations or weak related hints, but whose title and abstract lack clear CoT/self-consistency semantics.

Exclude records that have no 2023 version update, or whose current record lacks a direct publication clue.

arXiv ID
cue family
cue location
v1 submitted
first 2023 version
date of that version
primary category
publication clue

keep the shortest phrase that still preserves the current publication-status clue

date of the earliest 2023 version ascending

all corrected result pages are exhausted

for every surviving candidate, the current browse context, Comments field, and full Submission history are checked before emitting the final ordered table


Instruction: I am compiling a reading list of early vision Transformer papers. From arXiv papers whose first version (v1) was submitted in Q4 2020, I want to select those whose title or abstract explicitly uses vision- or image-related Transformer terminology and whose primary category is Computer Vision and Pattern Recognition. Additionally, eligible papers must have had at least one updated version in 2021, and their current arXiv record must still directly indicate a subsequent publication venue. Output a table sorted in ascending order by the date of each paper's earliest 2021 version, with columns: arXiv ID, cue family, cue location, v1 submitted, first 2021 version, date of that version, primary category, publication-trail source, current publication clue. If no qualifying papers exist, output NONE.

arXiv ID|cue family|cue location|v1 submitted|first 2021 version|date of that version|primary category|publication-trail source|current publication clue
2101.01097|Vision Transformer|Abstract-only|2020-12-30|v2|2021-01-08|cs.CV|Journal reference|IEEE Int. Conf. on Image Processing (ICIP) 2021
2012.12556|Visual Transformer; Vision Transformer|Title + Abstract|2020-12-23|v2|2021-01-15|cs.CV|Comments; Related DOI|Accepted by TPAMI 2022; 10.1109/TPAMI.2022.3152247
2012.00364|Image Processing Transformer|Title + Abstract|2020-12-01|v3|2021-05-28|cs.CV|Comments|CVPR 2021
2011.08019|Vision Transformer|Title + Abstract|2020-11-16|v2|2021-06-02|cs.CV|Comments|Accepted for Publication in IJCB2021
2010.11929|Vision Transformer|Abstract-only|2020-10-22|v2|2021-06-03|cs.CV|Comments|ICLR camera-ready version

Only include arXiv papers whose v1 submission date falls in Q4 2020.

The title or abstract must explicitly contain vision- or image-related Transformer terminology.

The current primary category must be cs.CV.

The paper must have had an updated version in 2021, and the current record must still directly indicate a subsequent publication venue.

candidate abstract pages yield v1 submitted date, first 2021 version, primary category, cue location, and publication-trail source

answer rows depend on deduplication plus detail-page validation

searches must cover both Title and Abstract because valid positives can appear only in the abstract

candidate eligibility must be determined by the original-submission date window rather than by arXiv-ID or announcement intuition

candidate filtering must use full Submission-history validation rather than most-recent-submission filtering

searches must use the broader semantic cue state rather than a ViT-only entry point, because ViT-only hits can be false positives

publication-trail false positives still require Comments / Journal reference / Related DOI validation before final inclusion

v1 submitted from 2020-10-01 through 2020-12-31; version-history event in 2021

Exclude records whose current primary category is not cs.CV and are only cross-listed.

Exclude records that contain only weak cues such as Transformer/ViT but lack explicit vision- or image-related terminology in the title and abstract.

Exclude records that had no version update in 2021 or whose current record contains no direct publication clue.

arXiv ID
cue family
cue location
v1 submitted
first 2021 version
date of that version
primary category
publication-trail source
current publication clue

record every qualifying family actually present and join multiple families with `; ` in the emitted field

date of the earliest 2021 version ascending

for every surviving candidate, the abstract page, current browse context, Comments field, Journal reference, Related DOI, and full Submission history are checked before emitting the final ordered table


Instruction: From arXiv papers whose first version was submitted in the first half of 2020, select those whose primary category is Machine Learning and whose title or abstract clearly focuses on certified robustness defenses. The research subject of each candidate paper must explicitly involve at least one of the following cues: randomized smoothing, de-randomized smoothing, transformation-specific smoothing, smoothed classifiers, certified defenses against backdoor/poisoning, or partition aggregation. Additionally, these papers must have a version update in 2021, and the current arXiv record must still show a formal publication or acceptance destination. Output a table sorted in ascending order by the date of each paper's earliest 2021 version, with columns: arXiv ID, defense family, threat/topic cue, cue location, v1 submitted, first 2021 version, date of that version, primary category, publication clue. If no qualifying items exist, output NONE.

arXiv ID|defense family|threat/topic cue|cue location|v1 submitted|first 2021 version|date of that version|primary category|publication clue
2002.10733|De-randomized smoothing; Randomized smoothing|patch attacks|Title + Abstract|2020-02-25|v3|2021-01-08|cs.LG|NeurIPS 2020
2006.04062|Smoothed classifiers; Randomized smoothing|certified L2 robustness|Title + Abstract|2020-06-07|v4|2021-01-08|cs.LG|NeurIPS 2020
2003.08904|RAB; Randomized smoothing|backdoor attacks|Title + Abstract|2020-03-19|v4|2021-02-26|cs.LG|IEEE Symposium on Security and Privacy 2023
2006.14768|Deep Partition Aggregation|general poisoning attacks; label-flipping poisoning attacks|Title + Abstract|2020-06-26|v2|2021-03-18|cs.LG|ICLR 2021
2002.12398|Transformation-specific smoothing; Randomized smoothing|semantic transformations|Title + Abstract|2020-02-27|v4|2021-09-17|cs.LG|2021 ACM SIGSAC Conference on Computer and Communications Security (CCS '21)

Only include arXiv papers with v1 submitted between 2020-01-01 and 2020-06-30.

The title or abstract must clearly focus on randomized smoothing, de-randomized smoothing, transformation-specific smoothing, smoothed classifiers, certified defenses against backdoor or poisoning attacks, or partition aggregation.

The current primary category must be cs.LG.

Must have a version update in 2021, and the current record must show a formal publication or acceptance destination.

candidate abstract pages yield v1 date, primary category, full Submission history, cue location, and current publication clue

final rows depend on deduplication plus detail-page validation

candidate filtering must use the original-submission window together with full 2021 version-history validation, because valid rows can have later newest versions

cross-listed hits whose primary category is not the target category must be excluded, with category validation on detail pages

searches must cover the broader certified-defense family, including partition-aggregation and backdoor/poisoning variants, rather than smoothing-only entries

topic+venue hits must be confirmed by detail-history validation that a 2021 version actually exists

v1 submitted from 2020-01-01 through 2020-06-30; version-history event in 2021

Exclude records whose current primary category is not cs.LG, even if semantically related.

Exclude records that are only broadly related to robustness, defense, or backdoor/poisoning but do not meet the required certified-defense semantics.

Exclude records without a 2021 version update or without a current publication clue.

arXiv ID
defense family
threat/topic cue
cue location
v1 submitted
first 2021 version
date of that version
primary category
publication clue

record every qualifying defense family actually present and join multiple families with `; ` in the emitted field

keep the shortest qualifying threat or topic cue that justifies inclusion

keep the shortest phrase that still preserves venue or publication-status information

for every surviving candidate, the abstract page, current browse context, Comments field, and full Submission history are checked before emitting the final ordered table


Instruction: Using the 2014–2018 American Community Survey 5-year estimates, identify North Carolina counties that simultaneously exhibit insufficient internet access, a high share of seniors living alone, limited transportation options, and low income. Candidate counties must meet all of the following conditions: at least 4,000 households with no internet access; a share of households with no internet access higher than the statewide share; a share of households with a householder aged 65 or older living alone higher than the statewide share; a share of households with no vehicle higher than the statewide share; and a median household income lower than the statewide median. All statewide comparisons must use the North Carolina statewide estimate from the same data release. List all qualifying counties in a single line ordered alphabetically by county name. Each county should be formatted as County|NoInternetHH|NoInternetRatePct|SeniorLivingAloneSharePct|NoVehicleRatePct|MedianIncome, with counties separated by commas. Percentage fields should retain three decimal places without a percent sign.

Use only the 2014-2018 American Community Survey 5-year estimates for North Carolina counties, and use the North Carolina statewide value from the same release as the comparison baseline.

Retain only counties with at least 4,000 households with no internet access, and where the share of households with no internet access, the share of households with a householder aged 65 or older living alone, and the share of households with no vehicle are all higher than the statewide shares, while the median household income is lower than the statewide median.

Output the final results in alphabetical order by county name, retaining the six required fields.

the 2014-2018 ACS 5-year product and North Carolina county geography yield the full county candidate set plus the statewide baseline row

the relevant ACS tables yield no-internet households, senior-living-alone share, no-vehicle share, and median household income for each county and the state baseline

final rows are produced only after the 5-year product/geography setup is fixed consistently and the full county filtering chain is executed from B28002 through the downstream filters

the 2018 ACS 5-year Detailed Tables product must be used instead of the 2018 ACS 1-year product

county coverage must be established under one consistent product-and-geography setup rather than by piecemeal fixes within the current table

all downstream county comparisons must use the same 5-year statewide baseline and the same all-counties geography

2014-2018 American Community Survey 5-year estimates for North Carolina counties, using the North Carolina statewide row from the same release as the comparison baseline

Exclude results that incorrectly use the 2018 ACS 1-Year Estimates Detailed Tables or any data other than the 2014-2018 5-year release.

Exclude results that do not use the North Carolina statewide row from the same release as the comparison baseline.

Exclude any county that does not satisfy every threshold or statewide comparison condition.

County NoInternetHH NoInternetRatePct SeniorLivingAloneSharePct NoVehicleRatePct MedianIncome

use the county display name including the word `County` and excluding a trailing state suffix

all percentage fields are plain numbers with exactly three decimal places and no percent sign

household counts and median income are emitted as plain integers without commas

the North Carolina statewide threshold is fixed once from the designated table

all 100 North Carolina counties are covered under the same table/product settings

every surviving county is validated against all four required tables before emission


Instruction: Using the 2015–2019 American Community Survey 5-year estimates, identify New Mexico counties that simultaneously exhibit large renter populations, notable language barriers, limited internet access, transportation disadvantages, low income, and rental stress. These counties are intended to assess which areas were unsuitable for replacing in-person tenant legal aid with online self-service before the pandemic. Candidate counties must meet all of the following conditions: at least 2,500 renter-occupied households; at least 500 limited-English-speaking households; a share of limited-English-speaking households higher than the statewide share; a share of households with no internet access higher than the statewide share; a share of households with no vehicle higher than the statewide share; a median household income lower than the statewide median; and rental stress higher than the statewide level. Rental stress higher than the statewide level must be demonstrated by at least one of the following: a share of renter households with a rent-to-income ratio of 35% or higher that exceeds the statewide share, or a share of renter households with more than one occupant per room that exceeds the statewide share. All comparisons of “higher than statewide” or “lower than statewide” use the New Mexico statewide value from the same data release. Output a single line with all qualifying counties sorted alphabetically by county name. Each county is written as County|RenterHH|LEPHH|LEPSharePct|NoInternetPct|NoVehiclePct|SevereRentBurdenPct|CrowdedRenterPct|MedianIncome|StressPath, where StressPath must be RENT, CROWD, or BOTH. Separate multiple counties with commas. Percentage fields retain three decimal places with no percent sign.

Use only New Mexico county data from the 2015–2019 American Community Survey 5-year estimates, and use the New Mexico statewide value from the same release as the comparison baseline.

Candidate counties must have at least 2,500 renter households, at least 500 limited-English-speaking households, and LEP share, no-internet share, and no-vehicle share all higher than the statewide values, while median household income is lower than the statewide value.

LEPHH must be computed by summing all four limited-English language groups in C16002. Retain only counties where at least one of Severe Rent Burden or Crowded Renter exceeds the statewide value, and write StressPath according to the rule.

the 2015-2019 ACS 5-year county geography yields New Mexico county candidates and the statewide baseline row

C16002 and the downstream housing/income tables yield LEPHH, LEP share, no-internet share, no-vehicle share, severe rent burden, crowded-renter share, and median income for counties and the state baseline

final rows are produced only after the LEPHH numerator is computed from all four limited-English language groups and the downstream stress-path filters are applied

the LEPHH numerator must be computed from all four limited-English language groups in C16002 rather than from Spanish-only counts

county eligibility and StressPath labels must be determined from the LEPHH measure and statewide LEP baseline defined under that full four-group calculation

the RENT / CROWD / BOTH classification must be determined by evaluating the same county set against both downstream housing-stress branches

2015-2019 American Community Survey 5-year estimates for New Mexico counties, using the New Mexico statewide row from the same release as the comparison baseline

Exclude results that treat Spanish-only limited-English households as the entire LEPHH count.

Exclude results that do not use the statewide baseline from the same release or do not recompute LEPHH/LEPSharePct/LEPShare_state.

Exclude counties that do not satisfy the final stress-path condition of RENT, CROWD, or BOTH.

County RenterHH LEPHH LEPSharePct NoInternetPct NoVehiclePct SevereRentBurdenPct CrowdedRenterPct MedianIncome StressPath

sum the four designated limited-English-proficient renter-household cells before emitting the integer total

emit BOTH only when both the severe-rent-burden and crowded-renter signals survive; otherwise emit the single surviving label

all candidate counties are validated under the same geography and product settings

the renter scope and LEP aggregation are checked before the final line is emitted


Instruction: Using the 2013–2017 American Community Survey 5-year estimates for California counties, identify counties where, for school meal material updates and annual parent information updates, relying solely on an English-only online portal is insufficient and paper materials with offline assistance must still be retained. Candidate counties must meet all of the following conditions: population aged 5–17 at least 75,000; among the population aged 5–17, the number of people who speak English "not well" or "not at all" is at least 2,000 and its share is higher than the statewide share; share of households without internet access higher than the statewide share; median household income lower than the statewide share; and at least two of three support-need signals higher than the statewide share. The three support-need signals are: share of households with no vehicle; share of minors living in households that received SSI, cash public assistance, or Food Stamps/SNAP in the past 12 months; and share of renter-occupied households with more than one occupant per room. All comparisons of "higher than statewide" or "lower than statewide" use the California statewide value from the same data release. Output all qualifying counties in a single line sorted alphabetically by county name. Each county is written as County|SchoolAgePop|SchoolAgeLEP|LEPSharePct|NoInternetPct|NoVehiclePct|ChildBenefitSharePct|CrowdedRenterPct|MedianIncome|SupportPath, where SupportPath uses only the three labels BENEFITS, CROWD, and MOBILITY joined by + in that fixed order. Multiple counties are separated by commas.

Use only California county data from the 2013–2017 American Community Survey 5-year estimates, and use the California statewide value from the same release as the comparison baseline.

Retain only counties with a population aged 5–17 of at least 75,000, a SchoolAgeLEP count of at least 2,000 with its share higher than the statewide share, a no-internet share higher than the statewide share, and a median household income lower than the statewide share.

SchoolAgeLEP must be the sum of the eight not-well/not-at-all cells across the four major language groups in B16004. The final county must also have at least two of the three offline support signals (Mobility, Benefits, Crowd) higher than the statewide share.

the 2013-2017 ACS 5-year county geography yields California county candidates and the statewide baseline row

B16004 and the downstream access/income/support tables yield SchoolAgeLEP, no-internet share, no-vehicle share, child-benefit share, crowded-renter share, and median income for counties and the statewide baseline

final rows are produced only after the SchoolAgeLEP numerator is computed from all eight 1980-target language cells and the downstream support-path branches are applied

the SchoolAgeLEP numerator must be computed from the full eight-cell language set in B16004 rather than from only two Spanish LEP cells

county eligibility and SupportPath labels must be determined from the LEP numerator and candidate set defined under that full eight-cell calculation

the BENEFITS / CROWD / MOBILITY path labels must be determined by applying all three downstream support branches to the same county set

2013-2017 American Community Survey 5-year estimates for California counties, using the California statewide row from the same release as the comparison baseline

Exclude results that use only the two Spanish LEP rows without merging all eight target cells.

Exclude results that do not rerun the B28011, B19013, B08201, B09010, and B25014 branches.

Exclude counties whose final SupportPath does not have at least two signals higher than the statewide share.

County SchoolAgePop SchoolAgeLEP LEPSharePct NoInternetPct NoVehiclePct ChildBenefitSharePct CrowdedRenterPct MedianIncome SupportPath

sum the eight designated school-age LEP cells before emitting the integer total

population counts and median income are emitted as plain integers without commas

use only BENEFITS, CROWD, and MOBILITY, and when multiple labels apply join them with `+` in the fixed order BENEFITS+CROWD+MOBILITY

the school-age LEP aggregation and all three support-signal gates are checked before the final line is emitted


Instruction: Using the 2012–2016 American Community Survey 5-year estimates, identify Pennsylvania counties with high elderly evacuation support needs that should conduct in-person outreach before winter storms. A candidate county must meet all of the following conditions: a population aged 65 and older of at least 15,000; a population aged 65 and older with ambulatory difficulty of at least 4,000, with its share of the 65+ population higher than the statewide share; a median household income below the statewide median; and at least two of four evacuation-support barriers above the statewide level. The four barriers are: share of households with a householder aged 65+ living alone, share of households with no vehicle, share of renter-occupied households with no vehicle, and mobile homes as a share of all housing units. All comparisons to the statewide level use the Pennsylvania statewide values from the same data release. Output all qualifying counties as a single line, sorted alphabetically by county name. Each county is formatted as County|SeniorPop|AmbulatoryDifficulty65Plus|AmbDiffPct|SeniorLivingAloneSharePct|NoVehiclePct|RenterNoVehiclePct|MobileHomePct|MedianIncome|BarrierPath, with counties separated by commas. For BarrierPath, use only ALONE, MOBILE_HOME, NO_VEHICLE, and RENTER_NO_VEHICLE, joined by + in that fixed order.

Use only Pennsylvania county data from the 2012–2016 American Community Survey 5-year estimates, and use the Pennsylvania statewide values from the same release as the comparison baseline.

Retain only counties with a 65+ population of at least 15,000, a 65+ ambulatory-difficulty population of at least 4,000 with a share above the statewide share, and a median household income below the statewide median.

The 65+ ambulatory-difficulty numerator must combine the four target cells from B18105 for ages 65–74 and 75+, male and female. The final county must also have at least two of the four barriers (Alone, No Vehicle, Renter No Vehicle, Mobile Home) above the statewide level.

ACS tables B01001 and B18105 yield county senior-population totals and the full 65+ ambulatory-difficulty numerator

statewide comparison tables yield Pennsylvania baselines plus county barrier metrics for living alone, no vehicle, renter no vehicle, mobile home, and median income

final rows are produced only after the county candidate set is defined from the full ambulatory-difficulty numerator and the downstream six-table filters are applied

the ambulatory-difficulty numerator must include the full 65+ population across 65–74 and 75+ and male and female cells rather than only the 75+ cells

downstream county eligibility and barrier labels must be determined from the county candidate set defined under that full ambulatory numerator

the at-least-two-barriers test must compare every county metric against the same Pennsylvania statewide baselines from the same ACS release

2012–2016 American Community Survey 5-year estimates for Pennsylvania counties, with Pennsylvania statewide baselines taken from the same release

Exclude results that take only the 75-and-over ambulatory-difficulty cells and mistakenly treat them as the full 65+ count.

Exclude results that do not use the Pennsylvania statewide baselines from the same release.

Exclude counties whose final BarrierPath does not satisfy at least two barriers above the statewide level.

County SeniorPop AmbulatoryDifficulty65Plus AmbDiffPct SeniorLivingAloneSharePct NoVehiclePct RenterNoVehiclePct MobileHomePct MedianIncome BarrierPath

use only ALONE, MOBILE_HOME, NO_VEHICLE, and RENTER_NO_VEHICLE, and when multiple labels apply join them with `+` in the fixed order ALONE+MOBILE_HOME+NO_VEHICLE+RENTER_NO_VEHICLE

the senior ambulatory-difficulty gate and all barrier-signal gates are checked before the final line is emitted


Instruction:
Identify South Texas climate divisions that were near-normal or wetter over two longer periods and still had light heating demand in March 1966. Restrict the search strictly to the four divisions South Central, Upper Coast, South, and Lower Valley, using the exact names and numbering order from the official Texas climate-division page fragment.

Retain only divisions that meet all three hard climate criteria: (1) the 10-month Palmer Z-Index anomaly ending February 1966 must be at least -0.05; (2) the 24-month Palmer Z-Index anomaly ending December 1966 must be at least -0.10; and (3) the actual March 1966 monthly Heating Degree Days value must be below 200°Df.

Sort qualifying divisions as follows: primary key is the sum of the February 1966 10-month Z-index anomaly and the December 1966 24-month Z-index anomaly, in descending order; secondary key is the March 1966 HDD actual value, in ascending order; tertiary key is the Texas division number, in ascending order.

Finally, output all matching divisions as a single comma-separated string. Each entry must follow the format "<Texas division number>|<division name>|<raw February 1966 10-month Z-index anomaly text>|<raw December 1966 24-month Z-index anomaly text>|<raw March 1966 monthly HDD actual value text>|<moisture score with two decimal places>", where the moisture score is the sum of the two Z-index anomalies. If no division qualifies, output NONE in uppercase.

Output all matching divisions as a single comma-separated string. Each entry must follow the format "<Texas division number>|<division name>|<raw February 1966 10-month Z-index anomaly text>|<raw December 1966 24-month Z-index anomaly text>|<raw March 1966 monthly HDD actual value text>|<moisture score with two decimal places>", where the moisture score is the sum of the two Z-index anomalies. If no division qualifies, output NONE in uppercase.

Filter only within the four South Texas divisions defined on the Texas divisional page: South Central, Upper Coast, South, and Lower Valley.

Retain divisions that simultaneously satisfy: 1966-02 10-month Palmer Z-Index anomaly >= -0.05; 1966-12 24-month anomaly >= -0.10; 1966-03 1-month Heating Degree Days Value < 200°Df.

Sort by sum of the two Z anomalies descending, then by 1966-03 HDD Value ascending, then by division number ascending.

The official Texas climate-division reference fixes the four South Texas division IDs and names that must be tracked across all later pages.

The 1966-02 10-month Palmer Z-Index anomaly page and the 1966-12 24-month Palmer Z-Index anomaly page jointly identify the four target South Texas divisions.

The 1966-03 1-month Heating Degree Days Value page supplies the last filter; final rows are produced only after intersecting the three divisional screens and then sorting by summed Z-anomaly score, HDD value, and division number.

Texas climate divisions South Central, Upper Coast, South, and Lower Valley; metrics are fixed to the 1966-02 10-month Palmer Z-Index anomaly, 1966-12 24-month anomaly, and 1966-03 1-month Heating Degree Days Value pages.

Exclude any Texas divisional results outside the four specified South Texas divisions.

Exclude results that misread the 1966-03 HDD page filter field as Anomaly instead of Value.

Exclude any division that fails any time window or threshold condition.

Canonical output uses the short Texas climate-division number; the scorer may accept an equivalent four-digit form, but the emitted oracle uses the short number.

The two Z-index anomaly fields and the March 1966 HDD field preserve the raw displayed text; comparison may ignore optional trailing HDD unit text such as `°Df`.

Compute as February 1966 10-month Z-index anomaly plus December 1966 24-month Z-index anomaly, formatted with exactly two decimal places.

March 1966 HDD actual value ascending after numeric normalization

Each Texas division is counted once per requested window; if any required window value is missing, exclude the division.

All candidate Texas divisions are checked under the same base period and anomaly windows.

The unique ordered output string is resolved before emission.


Instruction:
I am researching historical climate patterns in Texas and want to find similar historical years as references for shoulder-season operational planning. To do this, I need to identify specific Texas climate divisions in NOAA's Climate at a Glance database. The divisions I am looking for must have experienced a shift from a dry long-term moisture background back toward near-normal or wetter conditions in the early 20th century, and must have had light heating loads in two different shoulder-season windows.

Specifically, the selected divisions must satisfy all four of the following hard climate criteria: first, the 10-month Palmer Z-Index anomaly ending April 1907 must be no higher than -0.30; second, the 10-month Z-Index anomaly ending April 1923 must be no lower than -0.20; third, the 2-month Heating Degree Days (HDD) actual value ending May 1915 must be below 50°Df; and fourth, the 5-month HDD actual value ending October 1922 must be below 20°Df.

After identifying the qualifying divisions, sort them according to the following rules. The primary sort key is the "rebound score", defined as the April 1923 10-month Z-Index anomaly minus the April 1907 10-month Z-Index anomaly, sorted in descending order. In case of a tie, place the division with the smaller October 1922 5-month HDD actual value first. If still tied, place the division with the smaller Texas division number first.

Finally, output all matching divisions as a single comma-separated string. Each matching item must be formatted exactly as: "<Texas division number>|<4-digit division ID>|<division name>|<raw April 1907 10-month Z-Index anomaly text>|<raw April 1923 10-month Z-Index anomaly text>|<raw May 1915 2-month HDD actual value text>|<raw October 1922 5-month HDD actual value text>|<rebound score rounded to 2 decimal places>". If after checking all divisions none meet the combined criteria, output NONE in uppercase.

Output all matching divisions as a single comma-separated string. Each matching item must be formatted exactly as: "<Texas division number>|<4-digit division ID>|<division name>|<raw April 1907 10-month Z-Index anomaly text>|<raw April 1923 10-month Z-Index anomaly text>|<raw May 1915 2-month HDD actual value text>|<raw October 1922 5-month HDD actual value text>|<rebound score rounded to 2 decimal places>". If after checking all divisions none meet the combined criteria, output NONE in uppercase.

Retain only Texas divisions that satisfy all four historical conditions: the 10-month Palmer Z-Index anomaly for April 1907 must be no higher than -0.30; the 10-month anomaly for April 1923 must be no lower than -0.20; the 2-month HDD Value for May 1915 must be less than 50°Df; and the 5-month HDD Value for October 1922 must be less than 20°Df.

The rebound score is defined as the April 1923 10-month anomaly minus the April 1907 10-month anomaly.

Sort first by rebound score descending, then by the October 1922 5-month HDD Value ascending, then by Texas division number ascending.

Texas divisional pages yield the 1907-04 and 1923-04 10-month Palmer Z-Index anomalies plus the 1915-05 2-month and 1922-10 5-month HDD values for each division.

The rebound score and the two shoulder-season HDD values are computed only after the same set of candidate divisions survives all four historical filters.

The final output is produced after sorting the surviving divisions by rebound score, then 1922-10 HDD value, then division number.

The workflow must cover the 1907, 1923, and 1915 pages as well as the 1922-10 5-month HDD page.

Both HDD pages must be read from Value rather than Anomaly.

The final rebound ranking must be determined after the 1922-10 5-month HDD page excludes 7 South Central and 8 Upper Coast, using ReboundScore as the ordering basis.

Texas climate divisions evaluated on the 1907-04 and 1923-04 10-month Palmer Z-Index anomaly pages, the 1915-05 2-month HDD Value page, and the 1922-10 5-month HDD Value page.

Exclude results that stop after filtering only the 1907, 1923, and 1915 pages without also reading the 1922-10 5-month HDD page.

Exclude results that mistakenly read the 1915 or 1922 HDD page as Anomaly instead of Value.

Exclude any division that does not satisfy any one of the historical window conditions.

Emit the short Texas climate-division number in field 1 and the four-digit division ID in field 2.

The two Z-index anomaly fields preserve the raw displayed text; the two HDD fields preserve the raw displayed text, but comparison may ignore an optional trailing °Df suffix.

Compute as the April 1923 10-month Z-index anomaly minus the April 1907 10-month Z-index anomaly, formatted with exactly two decimal places.

October 1922 HDD actual value ascending after numeric normalization

The current oracle stores two equivalent answer sets: one preserves the HDD unit suffixes and one strips them; treat the two HDD columns as equivalent after optional °Df removal.

All candidate Texas divisions are checked under the same anomaly and HDD windows.


Instruction: Among the following five substances found in records related to painted products in Europe—aniline, cobalt, chromium, 3,3'-dichlorobenzidine, and dibenz[a,h]anthracene—select those whose pages explicitly contain the record 'Arts and crafts/Office supplies - body paint - tattoo ink, detected, Europe'. For each qualifying substance, produce a consolidated list that integrates historical keyword status and the deepest-level use categories. First, for each substance, count how many of the following five dated historical keywords are present (presence only, ignoring any leading numbers): Substances in Products - Canada (4/2014), Indirect additives food contact (10/2018), OEHHA Proposition 65 (3/2019), WA Children's Safe Product Act (4/2020), OEHHA Proposition 65 (1/2023). Also record the earliest and latest year-month among the hits. Second, for each substance, retain one category that best reflects its current use. After excluding the use category to which the above-mentioned painted-products record belongs, establish for each substance a terminal-use profile along two dimensions: 'daily consumer goods' (with the broad-category selection priority Formulation → Article → Occupation) and 'production materials' (with the broad-category selection priority Occupation → Article → Formulation). Finally, sort all substances by historical keyword hit count in descending order; for ties, sort by the number of products at the selected consumer-side node in descending order.

Output format: <DTXSID>|<hit_count>|<earliest>|<latest>|<consumer_kind>|<consumer_general_puc_id>|<consumer_terminal_puc_id>|<consumer_level>|<consumer_products>|<material_kind>|<material_general_puc_id>|<material_terminal_puc_id>|<material_level>|<material_products>. Results separated by commas; if no qualifying items exist, output NONE.

Screen only the five specified substances, and the target substance page must explicitly contain the record 'Arts and crafts/Office supplies - body paint - tattoo ink, detected, Europe'.

Count whether each of the five specified dated keywords appears, and record the earliest and latest year-month among the hits.

Then independently select one consumer-side route and one production-material-side route, the former following Formulation → Article → Occupation and the latter following Occupation → Article → Formulation, and refine each route separately to its deepest level.

each chemical page yields the five dated historical keywords, the Europe body-paint/tattoo-ink qualifying record, and the earliest/latest dated-hit months

PUC category paths yield one consumer-side route chosen by Formulation -> Article -> Occupation and one source-side route chosen independently by Occupation -> Article -> Formulation

final rows are produced only after the consumer-side and source-side routes are selected and refined separately for each chemical

the source-side route must be selected independently from the consumer-side route

for each chemical, the second route refinement must follow the production-material priority order rather than the daily-consumer route

family/type/general outputs must be determined only after both route selections are made independently

the five named substances as objectively existing chemicals, using their ChemExpo chemical pages, PUC category paths, keyword-state evidence, and Europe body-paint/tattoo-ink presence as relatively stable classification signals

Exclude substances that do not contain the specified Europe painted-products/tattoo-ink record.

Exclude results that directly reuse the same consumer-side route for the production-material side.

Exclude results that do not independently select and refine both routes according to the specified priority orders.

count only the exact dated keyword strings defined in the source rubric

the kind fields use the canonical labels Article, Formulation, or Occupation

the level fields use the canonical labels general, family, or type

the Products counts come from the local chemical-detail node for the selected branch, not from an overall aggregate shown elsewhere on the site


Instruction: In ChemExpo, for each of the six substances, determine whether it appears more often in material-contact uses or manufacturing-source uses, then select the most representative use category from the corresponding direction. Specifically, the scope is limited to these six substances: Di(2-Ethylhexyl) Phthalate, Melamine, Tris(2-Ethylhexyl) Phosphate, 4,5-Dihydro-2-Mercaptoimidazole, Phthalic Acid, and Triphenyl Phosphates Tert-Butylated. For each substance, three sets of data must be checked: first, a 'material-contact score' obtained by summing the occurrences of five specific labels (Indirect additives food contact (10/2018), Europe, Food contact items, Food contact items, Japan, entries containing 'Toys and children's products', and entries containing 'artificial_saliva' or 'artificial_sweat'); second, a 'manufacturing-source score' obtained by summing the occurrences of two specific labels (drinking_water, Europe, manufacturing, plastic_additive and Europe, plastic_additive); third, a regulatory profile generated by counting the total hits of four date-keyword labels (Indirect additives food contact (10/2018), OEHHA Proposition 65 (3/2019), WA Children's Safe Product Act (4/2020), OEHHA Proposition 65 (1/2023)) and extracting the earliest and latest month-year range spanned. Based on this, if the material-contact score is strictly greater than the manufacturing-source score, the substance is considered to favor material contact, and the most representative category is selected in the order Article → Formulation → Occupation; otherwise, it is considered to favor manufacturing source, and the selection order is Occupation → Article → Formulation. After determining the category, at each level, follow only the subcategory with the highest product count, continuing until no further subdivision is possible; if there is a tie at the same level, select the one with the smaller PUC ID.

Finally, sort the substances by datedHits in descending order; for ties, sort by Products of the selected node in descending order. Output format: <DTXSID>|<contactHits>|<sourceHits>|<datedHits>|<earliest>|<latest>|<prioritySide>|<PUCkind><generalPUCID>|<terminalPUCID>|<level>|<Products>|<PUCDocuments>|<PUCCuratedChemicals>. Results are separated by commas; if datedHits is 0, write NA for both earliest and latest.

Only consider the six specified substances, and first compute contactHits, sourceHits, and datedHits on the chemical page.

prioritySide must be determined by these historical signals first, then select the most representative current use category along the corresponding direction.

The current route selection must continue drilling down according to the given kind priority and the same-level smaller PUC ID rule.

each chemical page yields contactHits, sourceHits, datedHits, and the earliest/latest dated-hit months from the historical keyword set

after the priority side is chosen, the corresponding current PUC category path yields the representative kind, PUC path, product counts, and curated-chemical counts

final rows are produced only after the historical hit scores determine the side first and the current route is then chosen with the side-specific priority and tie-break rules

historical contact/source signal scores must take priority over current maximum-Products paths

the priority side must be determined from contactHits, sourceHits, and datedHits rather than from current visibility alone

PUC path tie-breaks are valid only after the correct side has been fixed and the route has been traced under that side's rules

the six named substances as objectively existing chemicals, using their ChemExpo chemical pages, PUC category paths, historical keyword families, and keyword-hit evidence as relatively stable classification signals

Exclude results that ignore historical signals and directly select the path with the maximum currently visible Products on the chemical page.

Exclude results that start PUC route selection without first determining the contact/source direction.

Exclude results where same-level tie-breaks do not follow the smaller PUC ID rule.

DTXSID contactHits sourceHits datedHits earliest latest prioritySide PUCkind generalPUCID terminalPUCID level Products PUCDocuments PUCCuratedChemicals

YYYY-MM; if datedHits is 0, earliest and latest are both `NA`

emit `contact` when contactHits > sourceHits; otherwise emit `source`

the PUCkind field uses the canonical labels Article, Formulation, or Occupation

the level field uses the canonical labels general, family, or type

Products comes from the selected terminal chemical-detail node

PUCDocuments and PUCCuratedChemicals come from the selected terminal PUC detail page Additional Statistics block


Instruction: I want to first screen for Formulation categories with many products in ChemExpo, and then from each category pick one specific product type that carries a child or aerosol label and is most worth prioritizing. Specifically, I am looking for a set of results: for each Formulation category with a total product count of at least 15,000, find one optimal specific product type. This target type must belong to the first family under that category's default display order that meets the following conditions: the family has a total product count of at least 3,000 and no assumed attributes, and it must have product types whose allowed attributes include 'aerosol' and 'child', with those types themselves having no assumed attributes. Then, among all qualifying 'aerosol' and 'child' types under that family, select the one with the highest product share (type product count divided by family cumulative product count). In case of a tie in share, prioritize in order: higher Product Count, more Curated Chemicals, or smaller PUC ID, until a single winner is determined. Finally, retain one final result: prefer the specific product type with the higher product share; if shares are equal, compare Product Count, Curated Chemicals, and PUC ID in that order, retaining the type with the higher count, more chemicals, or smaller ID, until a single final type is obtained.

Finally, output results sorted by final share descending, in the format: <general category PUC ID>|<family PUC ID>|<specific product type PUC ID>|<side>|<share>|<Curated Chemicals>. Join results with commas and no spaces. If no qualifying results exist, output NONE.

First, retain only Formulation categories with a total product count of at least 15,000.

In each candidate family, both an aerosol-side type and a child-side type must be found: the family itself must have no assumed attributes and a total product count of at least 3,000; both types must also have no assumed attributes.

All selections within a family and between the two sides must follow the specified tie-break rules: share, Product Count, Curated Chemicals, PUC ID.

The Formulation summary yields the high-product candidate families whose total product count is at least 15,000.

Within each family, lower-level type pages yield the aerosol and child candidates, their assumed/allowed attributes, product counts, shares, and curated-chemical counts.

Final rows are produced only after a family survives both aerosol and child branches and the winning type is selected by the layered share/product/chemical/PUC tie-break rules.

Both the aerosol and child branches within the same family must be fully checked before that family can be retained.

If one side is missing, the workflow must reject the family and continue to the next sibling family rather than stop at the first partial hit.

The final winner can be selected only after both sides have been independently optimized within the family and then compared against each other.

ChemExpo Formulation families as objectively existing catalog families, especially large families with total product count >= 15,000, evaluated by family/type identity and product/chemical counts as relatively stable catalog descriptors.

Exclude results that stop as soon as a usable type is found on either side, without requiring the same family to cover both aerosol and child sides.

Exclude results that stop as soon as one side is missing instead of continuing to check sibling families at the same level.

Exclude results that do not follow the specified layered tie-break rules to select the winning type within the family and as the final winner.

generalPUCID familyPUCID typePUCID side share CuratedChemicals

(generalPUCID, familyPUCID, typePUCID, side)

emit the lower-case branch label exactly as determined in the source workflow

canonical output uses the raw fraction `type Products / family Cumulative Products` rather than a decimal

emit the integer count from the selected type-level node

share descending by numeric fraction value

CuratedChemicals descending, then PUC IDs ascending as tie-breaks


Instruction: In ChemExpo, for the five substances Di(2-Ethylhexyl) Phthalate, Melamine, Phthalic Acid, Tris(2-Ethylhexyl) Phosphate, and Triphenyl Phosphates Tert-Butylated, check whether their historical labels are consistent with their current uses. The scope is limited to these five substances. For each substance, examine three groups of data. First, the material-contact score: check whether each of the following five label types appears, scoring 1 point per type: Indirect additives food contact (10/2018), Europe, Food contact items, Food contact items, Japan, any keyword containing Toys and children's products, and any keyword containing artificial_saliva or artificial_sweat. Second, the production-source score: check whether each of the following two label types appears, scoring 1 point per type: drinking_water, Europe, manufacturing, plastic_additive and Europe, plastic_additive. Third, dated-keyword hits: check only whether each of the following four keywords appears: Indirect additives food contact (10/2018), OEHHA Proposition 65 (3/2019), WA Children's Safe Product Act (4/2020), OEHHA Proposition 65 (1/2023); record the number of hits and the earliest and latest year-month among the hits. Then, if the material-contact score is strictly greater than the production-source score, treat it as leaning toward material contact and select a historically recommended category following the order Article → Formulation → Occupation; otherwise treat it as leaning toward production source and select following the order Occupation → Article → Formulation. After determining the category, follow the subcategory with the most products downward until no further subdivision is possible; if there is a tie at the same level, take the one with the smaller PUC ID. Separately, also find the current dominant category for this substance, ignoring historical clues, directly finding the path with the most products among all use categories, again following downward until no further subdivision is possible, breaking ties by smaller PUC ID. Finally, compare the two results: if the current dominant category is Article or Formulation and the historical clue also points to material contact, record ALIGNED; if the current dominant category is Occupation and the historical clue also points to production source, record ALIGNED; otherwise record MISALIGNED.

Output MISALIGNED records first; within the same group, sort by dated hits descending; if hits are equal, sort by current dominant category product count descending. Format: <DTXSID>|<materialHits>|<sourceHits>|<datedHits>|<earliest>|<latest>|<historySide>|<recommendedKind>|<recommendedGeneral>|<recommendedTerminal>|<recommendedLevel>|<recommendedProducts>|<currentKind>|<currentGeneral>|<currentTerminal>|<currentLevel>|<currentProducts>|<status>. Separate records with commas. If dated hits is 0, write NA for both earliest and latest.

DTXSID6020802|4|2|1|2018-10|2018-10|material|Article|Furniture and furnishings|Furniture and furnishings|general|15|Occupation|Raw materials|fireproof coatings|type|24|MISALIGNED,DTXSID8021484|3|1|1|2018-10|2018-10|material|Occupation|Laboratory supplies|Laboratory supplies|general|8|Occupation|Laboratory supplies|Laboratory supplies|general|8|MISALIGNED,DTXSID5020607|5|2|4|2018-10|2023-01|material|Article|Construction and building materials|flooring|family|1|Formulation|Home maintenance|paint|type|112|ALIGNED,DTXSID0021414|5|0|1|2018-10|2018-10|material|Article|Construction and building materials|Construction and building materials|general|3|Article|Construction and building materials|Construction and building materials|general|3|ALIGNED,DTXSID20872645|0|1|0|NA|NA|source|Occupation|Industrial products|hydraulic fluid|family|5|Occupation|Industrial products|hydraulic fluid|family|5|ALIGNED

Work only on the five specified substances, and first compute materialHits, sourceHits, and datedHits from the keyword-set blocks.

The historically recommended category must be derived independently from these historical signals; the current dominant category must be derived separately from the current use path.

Finally, compare only whether the broad direction of the historically recommended category and the current dominant category match, and mark ALIGNED or MISALIGNED accordingly.

each chemical page yields materialHits, sourceHits, datedHits, and the historical recommendation side implied by the keyword set

an independent current PUC category path yields the current dominant category, path labels, and classification counts for the same chemical

final rows are produced only after the historical recommendation and current dominant category are computed independently and then compared at the coarse side level

the historical recommendation must be determined from the keyword-set evidence rather than from the current largest category

historical recommendation must be computed from the keyword-set evidence before alignment is judged

ALIGNED / MISALIGNED status is valid only after the historical and current category directions have been derived independently and then compared

the five named substances as objectively existing chemicals, using their ChemExpo chemical pages, PUC category paths, and specified keyword-set evidence as relatively stable recommendation signals

Exclude results that only look at the current largest category and directly treat it as the historically recommended category.

Exclude results that default to ALIGNED for all cases without independently deriving the historically recommended category.

Exclude results that do not compute the historical and current paths separately but mix signals in the same route.

DTXSID materialHits sourceHits datedHits earliest latest historySide recommendedKind recommendedGeneral recommendedTerminal recommendedLevel recommendedProducts currentKind currentGeneral currentTerminal currentLevel currentProducts status

emit the lower-case historical winner `material` or `source`

these fields describe the historically suggested path

these fields describe the current dominant path


Instruction: For CFPB administrative enforcement actions filed between 2022-01-01 and 2024-12-31, produce a core-amount extraction report covering three product categories: Deposits, Furnishing, and Prepaid. For each category, identify the first action in the website’s default display order that satisfies all of the following: the action detail page provides both a Consent Order and a press release; the Products label corresponds exclusively and exactly to the target category; and the page explicitly states a consumer redress amount exceeding $5,000,000 along with a separate civil money penalty. After locating these three target actions, extract from each action’s Consent Order PDF the first occurrence of the consumer redress amount and the civil money penalty amount in the body text, along with their physical page numbers (PDF page 1 is p1).

Output the results strictly in alphabetical order of the product category’s English name, using the format <Product>|<Respondent>|<Docket>|<FilingDate>|<RedressUSD>|p<RedressPage>|<PenaltyUSD>|p<PenaltyPage>. Join entries with commas and no spaces. If no qualifying case is found for a category, output <Product>|NONE.

Consider only administrative enforcement actions on the CFPB Enforcement actions page whose Initial filing date falls between 2022-01-01 and 2024-12-31.

For each of the three product categories (Deposits, Furnishing, Prepaid), search top-down in the current default order for the first action that simultaneously satisfies: the page provides a Consent Order and a Press release; the Products label corresponds one-to-one with the target category; and the action detail page explicitly states consumer redress > $5,000,000 and also includes a civil money penalty.

Finally, extract from the correct Consent Order the first-occurrence amounts and page numbers for consumer redress and civil money penalty.

Enforcement-action list pages yield product-branch candidate actions in current default order.

Each action detail page and linked consent-order PDF yield product singularity, document-stack presence, redress and civil-money-penalty amounts, and first-occurrence pages.

Final rows are emitted only after identifying, for each product, the first action that survives detail-page and PDF validation.

List-level “has Consent Order + Press release” hits must be validated on the detail page for product singularity and money thresholds.

Prepaid candidates force document-level disambiguation among Consent Order, Stipulation, and terminating-order PDFs.

Per-product first-match selection must explicitly skip rejected candidates and validate downstream PDFs before it is finalized.

Administrative enforcement actions with Initial filing date from 2022-01-01 through 2024-12-31; current default case ordering and currently linked action documents.

Exclude cases where Products is not a single target category, or where the case appears relevant only at the list level but fails validation on the detail page.

Exclude cases that do not clearly satisfy redress > $5M and a separate civil money penalty.

Exclude cases that mistakenly use a Stipulation or Order Terminating the Consent Order in place of the required Consent Order.

Product Respondent Docket FilingDate RedressUSD RedressPage PenaltyUSD PenaltyPage

if a product has no qualifying case, emit `<Product>|NONE`

canonical output preserves the dollar sign and commas from the oracle, but equivalent comparison may ignore `$` and commas and compare the numeric USD amount

extract both money amounts from the Consent Order PDF only; do not substitute a stipulation, termination order, or other companion document

for each product, keep the first action in the default filtered order that fully satisfies every gate in the source rubric


Instruction: I want to organize the annual household financial stability survey reports in CFPB Reports, whose latest version page links directly to the 2023 and 2022 editions. I am interested in topics that appeared in the 2022 edition but no longer appear in subsequent annual reports, excluding the topic specifically used to mark "belongs to the same survey series." For each such topic, I need the following information: the earliest subsequent year in which it disappeared; the first report not belonging to this series among the results related to these topics from 2022 and earlier; the total number of related resources listed in that report, how many of them link back to the same survey series, how many of those linked reports have appendices with letter labels and at least three sections; and the title, publication date, and appendix letter range of the final selected report.

Output a single line per topic, sorted alphabetically by topic name: <topic>|<first-missing-year>|<pivot-report-or-NONE>|<pivot-date-or-NONE>|<direct-related-count>|<same-series-count>|<qualified-count>|<representative-report-or-NONE>|<publication-date-or-NONE>|<appendix-span-or-NONE>

Saving|2023|Consumer Savings App Strategies and Savings Outcomes|2022-12-07|4|2|1|Emergency Savings and Financial Security: Insights from the Making Ends Meet Survey and Consumer Credit Panel|2022-03-23|A-D,Student loans|2023|Report of the CFPB Education Loan Ombudsman|2022-10-20|0|0|0|NONE|NONE|NONE

First, identify the CFPB household financial well-being annual report series whose latest version links directly to the 2023 and 2022 editions.

Using the 2022 edition as the baseline, retain only topics that appear in 2022 but are no longer retained in subsequent annual reports, and exclude the topic specifically marked as "belongs to the same survey series."

For each topic, find the earliest non-series pivot report from 2022 or earlier, then count its directly listed related resources, how many link back to the same survey series, how many qualify under the appendix rule, and the appendix span of the final qualifying report.

current CFPB Reports pages yield the 2024 -> 2023 -> 2022 annual-survey chain and the 2022 topic baseline

report detail pages and report-search results yield first-disappearance year, pivot report title/date, related-resource counts, and links back to the annual-survey series

final rows are produced only after separating the first jump target from later return-to-series counting

requiring the pivot report itself to link back to the same survey series incorrectly rules out valid topic rows

title extraction must use the pivot detail page rather than search-result snippets

related-resource expansion must continue even when the pivot report itself does not link back to the same survey series

currently linked 2024, 2023, and 2022 CFPB household financial well-being annual reports; topic disappearance is judged over that three-year chain, and pivot-report search is restricted to 2022-and-earlier reports

Exclude topics that are still retained in subsequent annual reports.

Exclude results that incorrectly treat "the pivot report itself must link back to the same survey series" as a hard condition for column 3.

Exclude search results that directly belong to the same survey series or reports after 2022.

topic first-missing-year pivot-report-or-NONE pivot-date-or-NONE direct-related-count same-series-count qualified-count representative-report-or-NONE publication-date-or-NONE appendix-span-or-NONE

pivot-date and publication-date use YYYY-MM-DD when present

canonical span uses compact labels such as `A-D`

if qualified-count is 0, representative-report, publication-date, and appendix-span are all `NONE`


Instruction: I want to clearly organize the two waves of the Debt Collection Rule issued by the CFPB in 2020, including their respective proposal sources, delayed-effective-date arrangements, final effective dates, and the accompanying quick-reference PDF information on the implementation page. Specifically, I need the following: the exact issue date of each Final Rule; the originating proposed rule source and name, and the public comment deadline; whether the 2021 delayed-effective-date proposed rule applies to this batch of final rules, its public comment deadline, and the actual effective date that ultimately took effect; the quick-reference PDF file name on the official implementation page, and the first subheading in the body (excluding introductory or background paragraphs).

Output the two waves as a single-line string sorted by final-rule issue date from earliest to latest, identifying each wave by its final rule issue month (YYYY-MM):
<wave>|<final-rule-date>|<proposal-chain>|<proposal-comment-closes>|<delay-proposal-applied>|<delay-proposal-comments-close>|<controlling-effective-date>|<quick-reference-pdf>|<summary-heading>

2020-10|2020-10-30|2019-05-07 main|2019-09-18|YES|2021-05-19|2021-11-30|Executive summary of the October 2020 final rule|Coverage and Definitions,2020-12|2020-12-18|2019-05-07 main+2020-02-21 supplemental|2019-09-18+2020-08-04|YES|2021-05-19|2021-11-30|Executive summary of the December 2020 final rule|Validation Information Requirements and Disclosures

The two 2020 Debt Collection Practices (Regulation F) final rules that share the same name must be processed separately, not merged into one.

For each wave, independently confirm: the issue date, the corresponding proposal chain and public comment deadlines, whether the 2021 delayed-effective-date proposed rule covers it and its comment deadline, the final effective date, the corresponding implementation quick-reference PDF, and the first subheading in that PDF after skipping the introduction.

The detail page, proposal chain, and summary PDF for the October wave and the December wave must each be verified independently.

final-rules index pages and rule-development pages yield the two 2020 Regulation F final-rule waves and their proposal branches

each final-rule detail page, delayed-effective-date proposal page, and implementation quick-reference PDF yields the wave-specific dates, linked proposals, comment deadlines, and first summary heading

final rows are produced only after the October and December waves are traced separately end to end

two same-title final rules force correction from name-based collapsing to issue-date branching

proposal-chain validation must be done per wave; otherwise the later December chain contaminates the October row

implementation PDFs must be matched back to the correct wave rather than accepting the latest quick-reference page

the 2020-10 and 2020-12 Debt Collection Rule final-rule waves, their linked 2020 proposal pages, the 2021 delayed-effective-date proposal, and the current implementation-page quick-reference PDFs

Exclude results that merge the two identically named 2020 final rules into one.

Exclude results that project the December wave's proposal chain or executive-summary heading onto the October wave.

Exclude results that are not independently verified against each wave's own final-rule detail page and corresponding PDF.

wave final-rule-date proposal-chain proposal-comment-closes delay-proposal-applied delay-proposal-comments-close controlling-effective-date quick-reference-pdf summary-heading

wave is the final-rule issue month in YYYY-MM

all standalone date fields use YYYY-MM-DD

canonical chain joins proposal milestones with `+`, using pieces such as `2019-05-07 main` or `2020-02-21 supplemental` in chronological order

join multiple comment-close dates with `+` in the same order as proposal_chain

emit `YES` when a delay proposal is part of the chain; otherwise emit `NO`

use the displayed quick-reference PDF title, not the filename

use the first substantive heading from the final-rule PDF


Instruction: On the CFPB enforcement actions page, find the most recent 2024 administrative enforcement action that has since been terminated, separately for Credit Cards and Deposits, and align the termination order with the corresponding clause information in the original Consent Order. Specifically, identify one action for each of the two Products labels: Credit Cards and Deposits. The candidate pool is limited to administrative enforcement actions under Enforcement actions with an Initial filing date between 2024-01-01 and 2024-12-31. For each product label, select the action with the latest Initial filing date among those meeting all of the following conditions (if tied, pick the one with the smaller docket number): Initial filing date between 2024-01-01 and 2024-12-31; case status changed to Expired, Terminated, or Dismissed; the document set includes a Consent Order, a Stipulation, an Order Terminating the Consent Order, and a press release; and the Products label currently maps uniquely to the target product. For each selected action, extract the following from its Order Terminating the Consent Order: TerminationDate (the termination date); TerminationAuthority (the exact text of the termination authority at its first occurrence); TermAuthorityPage (the PDF physical page number where this text first appears). Additionally, find the first explicit reference to a paragraph number of the original Consent Order within the termination order text; if it reads "Paragraphs X and Y", take only X. Then go back to the original Consent Order and find the section heading that contains that paragraph.

Finally, output a single-line string in alphabetical order by product name: <Product>|<Respondent>|<Docket>|<InitialFilingDate>|<TerminationDate>|<TerminationAuthority>|p<TermAuthorityPage>|<ReferencedParagraph>|<ConsentSectionHeading>. If a product has no qualifying case, output: <Product>|NONE

Credit Cards|Apple Inc.|2024-CFPB-0012|2024-10-23|2025-09-22|12 U.S.C. § 5563(b)(3)|p1|83|ADMINISTRATIVE PROVISIONS,Deposits|Navy Federal Credit Union|2024-CFPB-0014|2024-11-07|2025-07-01|12 U.S.C. § 5563(b)(3)|p1|106|ADMINISTRATIVE PROVISIONS

Consider only administrative enforcement actions with an Initial filing date in 2024, and search for cases separately under Credit Cards and Deposits.

The case must have a status of Expired, Terminated, or Dismissed, and the page must simultaneously provide a Consent Order, Stipulation, Order Terminating the Consent Order, and Press release, with the Products label mapping one-to-one to the target category.

For each category, retain only the most recent qualifying case determined by Initial filing date, and compile the corresponding clause information from the termination order and the original Consent Order.

Enforcement-action list pages yield 2024 candidate actions by product branch.

Each action detail page and linked Consent Order, Stipulation, Order Terminating the Consent Order, and Press release documents yield status, initial filing date, single-product validity, and clause references.

Final rows are selected only after per-product comparison on Initial filing date and document-stack validation.

"Latest" must be determined by Initial filing date rather than termination date.

Product filters on the list page are insufficient until the action detail page confirms a single matching product.

Clause extraction in the terminating order must stay aligned with the original Consent Order rather than a superficially similar section.

Administrative enforcement actions with Initial filing date from 2024-01-01 through 2024-12-31; current action status and currently linked Consent Order, Stipulation, terminating-order, and Press release documents.

Exclude results that use termination date instead of Initial filing date to determine "latest".

Exclude cases with an incomplete document stack or where Products does not map to a single target category.

Exclude cases where the required clause correspondence between the terminating order and the original Consent Order cannot be established.

Product Respondent Docket InitialFilingDate TerminationDate TerminationAuthority TermAuthorityPage ReferencedParagraph ConsentSectionHeading

If a product has no qualifying case, emit `<Product>|NONE`

InitialFilingDate and TerminationDate use YYYY-MM-DD

Use the canonical statute string extracted from the termination order

TermAuthorityPage uses the canonical `pN` form

Emit the paragraph number as a plain numeric string

Emit the uppercase heading of the consent-order section containing the referenced paragraph


Instruction: I am preparing an internal outreach memo on historical complaint clusters and want to track a specific set of complaints: in the CFPB Complaint Database, identify complaints under the current product bucket covering credit reporting and other personal consumer reports, received between 2018-01-01 and 2021-12-31, tagged as Servicemember, with a public narrative, and whose text explicitly mentions medical bills or doctors. Please provide two representative complaint clusters: the first is the peak month of this cohort; the second is the latest month in this cohort whose complaint count is still at least half of the peak count. For each cluster, report the total complaint count for that month, the state with the highest complaint count in that month and its count, the company with the highest complaint count in that state and month and its count, and the earliest complaint received by that company in that state and month, including its ID, date, Issue, and Sub-issue. Output a single line with Peak first and LatestHalfPeak second, using the format: <Cluster>|<Month>|<MonthComplaints>|<TopState>|<StateComplaints>|<TopCompany>|<CompanyComplaints>|<EarliestComplaintID>|<EarliestDate>|<Issue>|<SubIssue>.

Output a single line with Peak first and LatestHalfPeak second, using the format: <Cluster>|<Month>|<MonthComplaints>|<TopState>|<StateComplaints>|<TopCompany>|<CompanyComplaints>|<EarliestComplaintID>|<EarliestDate>|<Issue>|<SubIssue>. Join the Peak and LatestHalfPeak clusters with a comma. For ties: months are ordered chronologically; states are ordered by state abbreviation alphabetically ascending, taking the first; companies are ordered by company name alphabetically ascending, taking the first; for the earliest complaint, if multiple complaints share the same date, take the one with the smallest Complaint ID.

Peak|2018-02|46|CA|28|TRANSUNION INTERMEDIATE HOLDINGS, INC.|9|2806154|2018-02-07|Improper use of your report|Reporting company used your report improperly,LatestHalfPeak|2021-11|23|CA|12|EQUIFAX, INC.|3|4928768|2021-11-19|Problem with a credit reporting company's investigation into an existing problem|Their investigation did not fix an error on your report

Only consider complaints under the current product bucket for credit reporting / other personal consumer reports, received from 2018-01-01 to 2021-12-31, tagged as Servicemember, with a public narrative, and whose text explicitly mentions medical bills or doctors.

The first cluster must be the peak month of this cohort; the second cluster must be the latest month in this cohort whose complaint count is at least half of the peak count.

For each cluster, recompute the top company within the corresponding month-state subset, then locate the earliest complaint received by that company in that state and month.

current complaint-form taxonomy yields the product bucket that contains credit reporting / other personal consumer reports

filtered complaint records yield month counts, month-state subsets, top companies, and earliest complaint details inside each chosen subset

final rows are produced only after peak month and latest-half-peak month are determined, and each downstream aggregate is computed inside its narrower month-state cohort

the latest month must be defined as the latest month whose count is at least ceil(PeakCount / 2)

overall top-company counts cannot be projected into a month-state subset without recomputation

earliest complaint selection is only valid after the month, state, and company filters are all frozen

Complaint Database records received from 2018-01-01 through 2021-12-31 under the current product-bucket mapping for the credit-reporting / other personal consumer reports family; Servicemember + public narrative + medical bills/doctors text cohort

Exclude results that directly treat the latest month with any complaints as the second cluster.

Exclude results that first find an overall top company in the full cohort and then project it onto a month-state subset.

Exclude results that do not first derive the half-peak threshold from PeakCount.

Cluster Month MonthComplaints TopState StateComplaints TopCompany CompanyComplaints EarliestComplaintID EarliestDate Issue SubIssue

use the uppercase two-letter state abbreviation

use the company display name as shown in the source data

for tied months use chronological order; for tied states use state-code alphabetical order; for tied companies use company-name alphabetical order; for the earliest complaint use EarliestDate ascending then Complaint ID ascending

emit `NONE` when the complaint row has no sub-issue value


Instruction:
I am collecting data for an internal crypto-complaint concentration memo and need to identify two representative complaint-month clusters from the CFPB complaint database and extract their core data records.
The data must come from a specific cohort: the product category must correspond to the current form’s bucket for Virtual currency, with the sub-product explicitly set to Virtual currency; the time window is strictly 2019-08-26 through 2022-08-26.
Within this cohort, the two characteristic months to identify are:
• Original peak month (AllCompanies): the month with the absolute highest complaint volume in the cohort without removing any company.
• Residual peak month (ResidualAfterTop2): after identifying the two companies with the highest complaint volume in the original peak month and completely removing all records of those two companies from the entire three-year analysis period, the month with the highest complaint volume that emerges from the remaining data.
For these two specific months, I need detailed attributes: total complaints for that month, the names of the two removed companies and their combined complaint contribution in the original peak month, the state with the highest complaint volume in that month and its volume, and the earliest complaint detail (including ID, date, Issue, and Sub-issue) for the most complained-about company within that state and month.
Please confirm the identity of these two months and output one formatted string per month with no spaces, in the order AllCompanies first, ResidualAfterTop2 second. The field sequence per row is strictly: <Cluster>|<Month>|<MonthComplaints>|<ExcludedTop2Companies>|<ExcludedTop2PeakMonthComplaints>|<TopState>|<StateComplaints>|<TopCompany>|<CompanyComplaints>|<EarliestComplaintID>|<EarliestDate>|<Issue>|<SubIssue>
Special notes:
• The <Cluster> field must be filled with AllCompanies or ResidualAfterTop2 accordingly.
• ExcludedTop2Companies and ExcludedTop2PeakMonthComplaints in both rows refer to the two companies identified from the original peak month and their combined complaint volume at that time.
• The four Earliest-related fields must correspond to the earliest complaint record for the top company in that state and month.
• To ensure a unique answer: output the two records in the order AllCompanies, ResidualAfterTop2; list ExcludedTop2Companies in descending order of their complaint volume in the original peak month, breaking ties alphabetically by field value; if the earliest complaint date is tied, take the one with the smallest Complaint ID.

Output one formatted string per row with no spaces, in the order AllCompanies first, ResidualAfterTop2 second. The field sequence per row is strictly: <Cluster>|<Month>|<MonthComplaints>|<ExcludedTop2Companies>|<ExcludedTop2PeakMonthComplaints>|<TopState>|<StateComplaints>|<TopCompany>|<CompanyComplaints>|<EarliestComplaintID>|<EarliestDate>|<Issue>|<SubIssue>.

AllCompanies|2021-05|336|Coinbase, Inc.+BAM Management US Holdings Inc.|226|CA|58|Coinbase, Inc.|21|4341451|2021-05-01|Money was not available when promised|NONE,ResidualAfterTop2|2020-06|180|Coinbase, Inc.+BAM Management US Holdings Inc.|226|OH|28|PNC Bank N.A.|22|3687376|2020-06-07|Other transaction problem|NONE

Only consider Complaint Database records under the current product bucket for Virtual currency, with sub-product Virtual currency, and received between 2019-08-26 and 2022-08-26.

First determine the AllCompanies original peak month, then identify the top two companies by complaint volume in that peak month.

These two companies must be completely removed from the entire three-year cohort, then recompute the residual peak month and all downstream fields based on the remaining cohort.

current complaint-form taxonomy yields the product bucket for Virtual currency

complaint records within the fixed three-year cohort yield the all-company peak month, per-month top companies, and residual-cohort recomputations after company removal

final rows are produced only after the top-2 companies from the original peak month are removed from the full cohort and downstream aggregates are computed on the resulting cohort

peak-month top companies must be removed from the entire cohort, not only from the original peak month

residual peak detection must be computed from the rewritten cohort rather than by local patching of the original month

downstream state / company / earliest-complaint fields are valid only after the global company-removal step is locked

Complaint Database records received from 2019-08-26 through 2022-08-26 under the current product-bucket mapping for Virtual currency

Exclude results that only locally delete the top-2 company records from the original peak month.

Exclude results that do not reapply the top-2 company set to the entire cohort before recomputing the residual peak.

Exclude downstream state, company, or earliest-complaint fields based on a cohort that has not been recomputed after removal.

Cluster Month MonthComplaints ExcludedTop2Companies ExcludedTop2PeakMonthComplaints TopState StateComplaints TopCompany CompanyComplaints EarliestComplaintID EarliestDate Issue SubIssue

join the two excluded company names with `+` in source-peak complaint-count descending order, breaking ties alphabetically

for the earliest complaint use EarliestDate ascending then Complaint ID ascending


Instruction:
I am writing an internal crypto-complaint sensitivity waterfall memo and need to extract four characteristic months (Clusters) at different filtering stages from the CFPB complaint database, along with their core data.

The underlying complaint cohort has clear boundaries: time is strictly limited to between 2020-01-01 and 2022-08-26, and it includes only complaint records classified under the "Virtual currency" subcategory within the current product taxonomy.

Based on this underlying cohort, I need to identify four characteristic peak months representing different successive exclusion stages:
• BaselinePeak: The absolute peak month with the highest total complaint volume in the initial complaint pool.
• AfterDominantPublicResponse: After globally removing all complaints related to the dominant Company public response from the BaselinePeak month, the new peak month in the remaining data pool.
• AfterDominantPublicResponseAndIssue: After further globally removing all complaints related to the dominant Issue from the second-round peak month, the third-round peak month formed from the remaining data.
• AfterDominantPublicResponseIssueAndCompany: After finally globally removing all complaints related to the most complained-about Company from the third-round peak month, the final peak month that emerges from the remaining data.

For these four characteristic months, I need their data under each respective filtering state: total complaints for that month, the new exclusion dimension triggered in this round (NewExclusionType) and its specific value (NewExclusionValue), the frequency of that excluded value in its "source peak month", the state with the highest complaint volume in that month and its complaint count, the most complained-about company within that state and its complaint count, and the detailed information (ID, date, Issue, and Sub-issue) of the earliest complaint received by that top company in that state and characteristic month.

Please confirm the identity and data for these four characteristic months, and output four formatted strings with no spaces, strictly in the specific order: BaselinePeak, AfterDominantPublicResponse, AfterDominantPublicResponseAndIssue, AfterDominantPublicResponseIssueAndCompany. The field sequence for each row must be fixed as:
<Cluster>|<Month>|<MonthComplaints>|<NewExclusionType>|<NewExclusionValue>|<NewExclusionCountInSourcePeak>|<TopState>|<StateComplaints>|<TopCompany>|<CompanyComplaints>|<EarliestComplaintID>|<EarliestDate>|<Issue>|<SubIssue>

For ties, always select in ascending lexicographic order; for the earliest complaint with the same date, select by ascending Complaint ID numerically. Normalize all empty values to NONE before sorting.

Output four formatted strings with no spaces, strictly in the specific order: BaselinePeak, AfterDominantPublicResponse, AfterDominantPublicResponseAndIssue, AfterDominantPublicResponseIssueAndCompany. The field sequence for each row must be fixed as: <Cluster>|<Month>|<MonthComplaints>|<NewExclusionType>|<NewExclusionValue>|<NewExclusionCountInSourcePeak>|<TopState>|<StateComplaints>|<TopCompany>|<CompanyComplaints>|<EarliestComplaintID>|<EarliestDate>|<Issue>|<SubIssue>. For ties, always select in ascending lexicographic order; for the earliest complaint with the same date, select by ascending Complaint ID numerically. Normalize all empty values to NONE before sorting.

BaselinePeak|2021-05|336|NONE|NONE|0|CA|58|Coinbase, Inc.|21|4341451|2021-05-01|Money was not available when promised|NONE,AfterDominantPublicResponse|2022-07|31|Company public response|NONE|321|CA|6|BANK OF AMERICA, NATIONAL ASSOCIATION|4|5735227|2022-07-05|Other transaction problem|NONE,AfterDominantPublicResponseAndIssue|2021-10|13|Issue|Fraud or scam|26|TX|2|BANK OF AMERICA, NATIONAL ASSOCIATION|1|4821611|2021-10-18|Other transaction problem|NONE,AfterDominantPublicResponseIssueAndCompany|2021-06|8|Company|Foris DAX, Inc.|5|NC|3|TRUIST FINANCIAL CORPORATION|2|4462160|2021-06-15|Money was not available when promised|NONE

Only consider Complaint Database records under the Virtual currency subcategory within the current product bucket, with a received date between 2020-01-01 and 2022-08-26.

Sequentially determine the peak months for the four stages: BaselinePeak, AfterDominantPublicResponse, AfterDominantIssue, and AfterDominantCompany, as required by the task.

Each identified dominant Company public response, dominant Issue, and dominant Company must be written back to the corresponding upstream residual cohort, globally removed, and then the next round of statistics must be recalculated.

complaint records yield the baseline peak plus the dominant Company public response, dominant Issue, and dominant Company at successive residual-peak states

final rows are produced only after each dominant value is applied to the full upstream residual cohort and then used to determine the next peak

dominant values found in a peak month cannot be deleted only inside that month; each must be propagated to the full upstream cohort

each new residual peak depends on the prior round's global rewrite, so local month-by-month deletion gives the wrong chain

downstream counts and exemplar complaints are valid only after the full three-stage waterfall is executed

Complaint Database records received from 2020-01-01 through 2022-08-26 under the current product-bucket mapping for Virtual currency

Exclude results that only delete records locally within the current peak month without writing back to the entire upstream cohort.

Exclude results that do not re-find the next peak month on each round's residual cohort.

Exclude intermediate or final fields based on downstream statistics that were not recalculated globally.

Cluster Month MonthComplaints NewExclusionType NewExclusionValue NewExclusionCountInSourcePeak TopState StateComplaints TopCompany CompanyComplaints EarliestComplaintID EarliestDate Issue SubIssue

emit the canonical exclusion-type label from the source workflow; when no new exclusion is applied, emit `NONE`

emit the canonical excluded value; when no value exists, emit `NONE`

normalize empty values to `NONE` before sorting; for other ties use lexicographic order ascending, and for the earliest complaint use EarliestDate ascending then Complaint ID ascending


Instruction:
I want to consolidate into a single line the admission variables actually used by several prediction schemes in this 2020 COVID-19 mortality risk prediction paper, so that it is easy to see which variables overlap across schemes and which appear only in the comparator schemes.
Specifically, the target paper is a 2020 open-access review whose title contains all three terms "prognosis model", "mortality risk", and "COVID-19", and whose main text discusses Wuhan inpatients.
The variables to retain fall into two categories: (1) admission variables that appear in both the Random Forest and XGBoost variable importance rankings; (2) admission variables that do not appear in both rankings but are additional variables present in the CURB-65 or NEWS schemes used by the authors as comparators.
Format each variable as follows:
variable~RF_rank_or_0~XGB_rank_or_0~F~C~N~X
where:
	•	RF is the variable's rank in the Random Forest ranking, or 0 if absent;
	•	XGB is the variable's rank in the XGBoost ranking used in the paper, or 0 if absent;
	•	F, C, N, X indicate, respectively, whether the variable entered the authors' final logistic regression model, belongs to CURB-65, belongs to NEWS, and belongs to the XGBoost model used as a comparator in the paper.
Finally, sort all variables alphabetically by variable name, join them with | into a single line; if the target paper cannot be found, output NONE.

First locate the target paper: a 2020 open-access review whose title contains all three terms "prognosis model", "mortality risk", and "COVID-19", and whose main text discusses Wuhan inpatients.

Retain only two categories of variables: admission variables that appear in both the Random Forest and XGBoost rankings; or admission variables that, although not appearing in both rankings, belong to the CURB-65 or NEWS comparator schemes.

For each variable, annotate the RF rank, XGB rank, and whether it belongs to the final logistic regression, CURB-65, NEWS, and the comparator XGBoost model as required, then output sorted alphabetically by variable name.

Europe PMC search results and the target open-access article yield the candidate Wuhan COVID mortality-risk review

article tables and discussion sections yield RF/XGBoost rankings, final logistic-regression inclusion, and comparator-variable memberships for CURB-65 / NEWS / external XGBoost

final rows are produced only after overlapping variables and comparator-only variables are merged into one normalized variable table

comparator-only variables must be captured from the discussion definitions rather than from Table 2 + Table 3 alone

comparator membership must be read from the article's explicit scheme definitions rather than inferred from common clinical usage

variable normalization must wait until RF/XGBoost overlap and comparator-only additions are both complete

the 2020 open-access COVID-19 mortality-risk review about Wuhan inpatients; variable inclusion and comparator definitions are taken from the article's current full-text tables and discussion

Exclude admission variables that appear in neither both RF and XGB rankings nor the CURB-65 / NEWS additional variables.

Exclude results that stop at Table 2 + Table 3 without proceeding to the discussion to read the comparator definitions.

Exclude results that infer comparator variables from common knowledge rather than verifying against the paper's explicit definitions.

variable RF_rank_or_0 XGB_rank_or_0 F C N X

use the variable wording normalized in the article tables; keep `Systolic pressure` and `Diastolic pressure` as separate variables

RF_rank_or_0 and XGB_rank_or_0 are positive integer ranks when present, otherwise 0

F, C, N, and X are emitted as 1 or 0 only

the shared variable set from Table 2 and Table 3 is fully resolved

the comparator definitions for CURB-65, NEWS, and the comparison XGBoost model are fixed before the final line is emitted


Instruction: Track Phase 1 studies from the 2021 Alzheimer's disease drug development pipeline review by ClinicalTrials.gov identifier, and check whether they persist in the 2022 and 2023 open-access reviews of the same series. Only include studies that were Phase 1 in the 2021 review and can still be found under the same ClinicalTrials.gov identifier in both the 2022 and 2023 reviews. Do not rely on drug names alone, as they may change. Output all qualifying studies as a single line in the format: NCT~2021 drug~2022 phase~2022 drug~2023 phase~2023 drug, sorted by NCT ascending and joined by |. If the three papers cannot be found, output NONE.

Output all qualifying studies as a single line in the format: NCT~2021 drug~2022 phase~2022 drug~2023 phase~2023 drug, sorted by NCT ascending and joined by |. If no qualifying studies exist, output NONE.

Only use Phase 1 studies from the 2021 Alzheimer's disease drug development pipeline article as the baseline.

Whether a study is retained in 2022 and 2023 must be determined by exact matching on the same ClinicalTrials.gov NCT identifier, not by drug name alone.

Only retain studies that can be found under the same NCT in both 2022 and 2023, and output them sorted by NCT ascending.

the 2021, 2022, and 2023 pipeline reviews yield the 2021 Phase 1 baseline cohort and the later-year lookup tables

article tables yield exact ClinicalTrials.gov NCT IDs, yearly phase labels, and yearly drug names for the same study

final rows are produced only after 2021 baseline studies are re-identified in both later reviews by exact NCT matching

continuation must be matched by exact NCT rather than by drug name, because drug names drift across years

2022 and 2023 lookups must use the fixed 2021 Phase 1 NCT set as the starting cohort rather than later-year table membership alone

yearly phase/name fields are only valid after the same NCT is confirmed across all three reviews

the 2021, 2022, and 2023 open-access Alzheimer's disease drug development pipeline reviews; the baseline is the 2021 Phase 1 cohort and continuation is matched by exact ClinicalTrials.gov NCT

Exclude results that are continued only by drug name without exact matching on the same ClinicalTrials.gov identifier.

Exclude studies found only in 2022 or only in 2023, not continuously retained in both years.

Exclude studies not in the 2021 baseline Phase 1 set.

use the exact ClinicalTrials.gov identifier as the cross-year identity key

normalize phases to P1, P2, or P3; if the same NCT appears multiple times in a year, keep the highest phase for that year

keep the agent text as shown in each year-specific table