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)

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["Root searches yield candidate arXiv IDs","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"],"control_dependency":["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"],"freeze":{"historical_window":"v1 year in {2019, 2020, 2021}"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":"\n","header_row":"required literal first row shown in the oracle","schema":["arXiv ID","term family","cue location","v1 submitted","first next-year version","date of that version","primary category","publication-trail source","current publication clue"],"dedup_key":"bare arXiv ID without version suffix","date_format":"YYYY-MM-DD","term_family_extraction":{"allowed_families":["Distillation","Condensation"],"tie_break":"if both families appear, prefer the title hit; if both appear only in the abstract, keep the first abstract occurrence"},"cue_location":{"Title":"qualifying cue appears only in the title","Abstract-only":"qualifying cue appears only in the abstract","Title + Abstract":"qualifying cue appears in both title and abstract, or title carries only a generic cue while the abstract carries the specific family name"},"publication_trail_source":["Comments","Journal reference","Related DOI"],"current_publication_clue":"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","sorting_or_selection":{"primary":"v1 submitted ascending","secondary":"arXiv ID ascending"},"stop_condition":["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

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["root searches yield candidate arXiv IDs","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"],"control_dependency":["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"],"freeze":{"historical_window":"v1 submitted in 2022; version-history event in 2023"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":"\n","header_row":"required literal first row shown in the oracle","schema":["arXiv ID","cue family","cue location","v1 submitted","first 2023 version","date of that version","primary category","publication clue"],"dedup_key":"bare arXiv ID without version suffix","date_format":"YYYY-MM-DD","cue_family":["Chain-of-thought","Self-consistency"],"cue_location":{"Title":"qualifying cue appears only in the title","Abstract-only":"qualifying cue appears only in the abstract","Title + Abstract":"qualifying cue appears in both title and abstract, or title carries only a generic cue while the abstract carries the specific family name"},"publication_clue":{"source":"Comments field only","extraction":"keep the shortest phrase that still preserves the current publication-status clue"},"sorting_or_selection":{"primary":"date of the earliest 2023 version ascending","secondary":"arXiv ID ascending"},"stop_condition":["all corrected result pages are exhausted","all unique candidate IDs are opened to abstract-page depth","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

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["root searches yield candidate arXiv IDs","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"],"control_dependency":["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"],"freeze":{"historical_window":"v1 submitted from 2020-10-01 through 2020-12-31; version-history event in 2021"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":"\n","header_row":"required literal first row shown in the oracle","schema":["arXiv ID","cue family","cue location","v1 submitted","first 2021 version","date of that version","primary category","publication-trail source","current publication clue"],"dedup_key":"bare arXiv ID without version suffix","date_format":"YYYY-MM-DD","cue_family_extraction":"record every qualifying family actually present and join multiple families with `; ` in the emitted field","cue_location":{"Title":"qualifying cue appears only in the title","Abstract-only":"qualifying cue appears only in the abstract","Title + Abstract":"qualifying cue appears in both title and abstract, or title carries only a generic cue while the abstract carries the specific family name"},"publication_trail_source":["Comments","Journal reference","Related DOI"],"current_publication_clue":"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","sorting_or_selection":{"primary":"date of the earliest 2021 version ascending","secondary":"arXiv ID ascending"},"stop_condition":["all 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, 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)

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["root searches yield candidate arXiv IDs","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"],"control_dependency":["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"],"freeze":{"historical_window":"v1 submitted from 2020-01-01 through 2020-06-30; version-history event in 2021"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":"\n","header_row":"required literal first row shown in the oracle","schema":["arXiv ID","defense family","threat/topic cue","cue location","v1 submitted","first 2021 version","date of that version","primary category","publication clue"],"dedup_key":"bare arXiv ID without version suffix","date_format":"YYYY-MM-DD","defense_family_extraction":"record every qualifying defense family actually present and join multiple families with `; ` in the emitted field","threat_topic_cue":"keep the shortest qualifying threat or topic cue that justifies inclusion","cue_location":{"Title":"qualifying cue appears only in the title","Abstract-only":"qualifying cue appears only in the abstract","Title + Abstract":"qualifying cue appears in both title and abstract, or title carries only a generic cue while the abstract carries the specific family name"},"publication_clue":"keep the shortest phrase that still preserves venue or publication-status information","sorting_or_selection":{"primary":"date of the earliest 2021 version ascending","secondary":"arXiv ID ascending"},"stop_condition":["all 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, 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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["County","NoInternetHH","NoInternetRatePct","SeniorLivingAloneSharePct","NoVehicleRatePct","MedianIncome"],"county":"use the county display name including the word `County` and excluding a trailing state suffix","dedup_key":"county FIPS / GEOID","percentage_fields":"all percentage fields are plain numbers with exactly three decimal places and no percent sign","numeric_fields":"household counts and median income are emitted as plain integers without commas","sorting_or_selection":"county name alphabetical ascending","stop_condition":["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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["County","RenterHH","LEPHH","LEPSharePct","NoInternetPct","NoVehiclePct","SevereRentBurdenPct","CrowdedRenterPct","MedianIncome","StressPath"],"county":"use the county display name including the word `County` and excluding a trailing state suffix","dedup_key":"county FIPS / GEOID","LEPHH":"sum the four designated limited-English-proficient renter-household cells before emitting the integer total","percentage_fields":"all percentage fields are plain numbers with exactly three decimal places and no percent sign","numeric_fields":"household counts and median income are emitted as plain integers without commas","StressPath":{"allowed_values":["RENT","CROWD","BOTH"],"rule":"emit BOTH only when both the severe-rent-burden and crowded-renter signals survive; otherwise emit the single surviving label"},"sorting_or_selection":"county name alphabetical ascending","stop_condition":["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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"2013-2017 American Community Survey 5-year estimates for California counties, using the California statewide row from the same release as the comparison baseline"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["County","SchoolAgePop","SchoolAgeLEP","LEPSharePct","NoInternetPct","NoVehiclePct","ChildBenefitSharePct","CrowdedRenterPct","MedianIncome","SupportPath"],"county":"use the county display name including the word `County` and excluding a trailing state suffix","dedup_key":"county FIPS / GEOID","SchoolAgeLEP":"sum the eight designated school-age LEP cells before emitting the integer total","percentage_fields":"all percentage fields are plain numbers with exactly three decimal places and no percent sign","numeric_fields":"population counts and median income are emitted as plain integers without commas","SupportPath":"use only BENEFITS, CROWD, and MOBILITY, and when multiple labels apply join them with `+` in the fixed order BENEFITS+CROWD+MOBILITY","sorting_or_selection":"county name alphabetical ascending","stop_condition":["all candidate counties are validated under the same geography and product settings","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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"2012–2016 American Community Survey 5-year estimates for Pennsylvania counties, with Pennsylvania statewide baselines taken from the same release"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["County","SeniorPop","AmbulatoryDifficulty65Plus","AmbDiffPct","SeniorLivingAloneSharePct","NoVehiclePct","RenterNoVehiclePct","MobileHomePct","MedianIncome","BarrierPath"],"county":"use the county display name including the word `County` and excluding a trailing state suffix","dedup_key":"county FIPS / GEOID","percentage_fields":"all percentage fields are plain numbers with exactly three decimal places and no percent sign","numeric_fields":"population counts and median income are emitted as plain integers without commas","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","sorting_or_selection":"county name alphabetical ascending","stop_condition":["all candidate counties are validated under the same geography and product settings","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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data","intra_chain":true,"inter_chain":true,"data_dependency":["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."],"freeze":{"historical_window":"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."},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["texas_division_no","division_name","feb_1966_10mo_z_text","dec_1966_24mo_z_text","mar_1966_hdd_text","moisture_score"],"texas_division_no":"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.","raw_text_fields":"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`.","moisture_score":"Compute as February 1966 10-month Z-index anomaly plus December 1966 24-month Z-index anomaly, formatted with exactly two decimal places.","sorting_or_selection":{"primary":"moisture_score descending","secondary":"March 1966 HDD actual value ascending after numeric normalization","tertiary":"Texas division number ascending"},"eligibility_rule":"Each Texas division is counted once per requested window; if any required window value is missing, exclude the division.","stop_condition":["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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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."],"control_dependency":["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."],"freeze":{"historical_window":"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."},"answer_type":"mixed multi-line output"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["texas_division_no","division_id_4digit","division_name","apr_1907_10mo_z_text","apr_1923_10mo_z_text","may_1915_2mo_hdd_text","oct_1922_5mo_hdd_text","rebound_score"],"texas_division_no":"Emit the short Texas climate-division number in field 1 and the four-digit division ID in field 2.","raw_text_fields":"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.","rebound_score":"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.","sorting_or_selection":{"primary":"rebound_score descending","secondary":"October 1922 HDD actual value ascending after numeric normalization","tertiary":"Texas division number ascending"},"answer_set_note":"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.","stop_condition":["All candidate Texas divisions are checked under the same anomaly and HDD windows.","The unique ordered output string is resolved before emission."]}}


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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["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"],"dated_hits":"count only the exact dated keyword strings defined in the source rubric","date_format":"YYYY-MM","kind":"the kind fields use the canonical labels Article, Formulation, or Occupation","level":"the level fields use the canonical labels general, family, or type","products_source":"the Products counts come from the local chemical-detail node for the selected branch, not from an overall aggregate shown elsewhere on the site","sorting_or_selection":{"primary":"hit_count descending","secondary":"consumer_products descending","tertiary":"DTXSID ascending"}}}


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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["DTXSID","contactHits","sourceHits","datedHits","earliest","latest","prioritySide","PUCkind","generalPUCID","terminalPUCID","level","Products","PUCDocuments","PUCCuratedChemicals"],"dated_hits":"count only the exact dated keyword strings defined in the source rubric","date_format":"YYYY-MM; if datedHits is 0, earliest and latest are both `NA`","prioritySide":"emit `contact` when contactHits > sourceHits; otherwise emit `source`","kind":"the PUCkind field uses the canonical labels Article, Formulation, or Occupation","level":"the level field uses the canonical labels general, family, or type","products_source":"Products comes from the selected terminal chemical-detail node","terminal_statistics":"PUCDocuments and PUCCuratedChemicals come from the selected terminal PUC detail page Additional Statistics block","sorting_or_selection":{"primary":"datedHits descending","secondary":"Products descending","tertiary":"DTXSID ascending"}}}


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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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."],"control_dependency":["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."],"freeze":{"historical_window":"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."},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["generalPUCID","familyPUCID","typePUCID","side","share","CuratedChemicals"],"dedup_key":"(generalPUCID, familyPUCID, typePUCID, side)","side":"emit the lower-case branch label exactly as determined in the source workflow","share":"canonical output uses the raw fraction `type Products / family Cumulative Products` rather than a decimal","CuratedChemicals":"emit the integer count from the selected type-level node","sorting_or_selection":{"primary":"share descending by numeric fraction value","secondary":"type-level Products descending","tertiary":"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

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["DTXSID","materialHits","sourceHits","datedHits","earliest","latest","historySide","recommendedKind","recommendedGeneral","recommendedTerminal","recommendedLevel","recommendedProducts","currentKind","currentGeneral","currentTerminal","currentLevel","currentProducts","status"],"dated_hits":"count only the exact dated keyword strings defined in the source rubric","date_format":"YYYY-MM; if datedHits is 0, earliest and latest are both `NA`","historySide":"emit the lower-case historical winner `material` or `source`","recommended_fields":"these fields describe the historically suggested path","current_fields":"these fields describe the current dominant path","kind":"the kind fields use the canonical labels Article, Formulation, or Occupation","level":"the level fields use the canonical labels general, family, or type","status":"emit `ALIGNED` or `MISALIGNED` exactly","sorting_or_selection":{"primary":"status with MISALIGNED before ALIGNED","secondary":"datedHits descending","tertiary":"currentProducts descending","quaternary":"DTXSID ascending"}}}


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.

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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."],"control_dependency":["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."],"freeze":{"historical_window":"Administrative enforcement actions with Initial filing date from 2022-01-01 through 2024-12-31; current default case ordering and currently linked action documents."},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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 > $5M and a separate civil money penalty.","Exclude cases that mistakenly use a Stipulation or Order Terminating the Consent Order in place of the required Consent Order."],"normalization":{"field_separator":"|","record_separator":",","schema":["Product","Respondent","Docket","FilingDate","RedressUSD","RedressPage","PenaltyUSD","PenaltyPage"],"product_order":"alphabetical by Product name","fallback_row":"if a product has no qualifying case, emit `<Product>|NONE`","date_format":"FilingDate uses YYYY-MM-DD","money_fields":"canonical output preserves the dollar sign and commas from the oracle, but equivalent comparison may ignore `$` and commas and compare the numeric USD amount","page_fields":"page fields use the canonical `pN` form","document_scope":"extract both money amounts from the Consent Order PDF only; do not substitute a stipulation, termination order, or other companion document","selection_rule":"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

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["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"],"topic_order":"alphabetical by topic name","date_format":"pivot-date and publication-date use YYYY-MM-DD when present","appendix_span":"canonical span uses compact labels such as `A-D`","none_rule":"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

{"State-Gated Retrieval":["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."],"dependency_type":"Data + Control","intra_chain":true,"inter_chain":true,"data_dependency":["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"],"control_dependency":["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"],"freeze":{"historical_window":"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"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["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."],"exclusion_conditions":["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."],"normalization":{"field_separator":"|","record_separator":",","schema":["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":"wave is the final-rule issue month in YYYY-MM","date_format":"all standalone date fields use YYYY-MM-DD","proposal_chain":"canonical chain joins proposal milestones with `+`, using pieces such as `2019-05-07 main` or `2020-02-21 supplemental` in chronological order","proposal_comment_closes":"join multiple comment-close dates with `+` in the same order as proposal_chain","delay_proposal_applied":"emit `YES` when a delay proposal is part of the chain; otherwise emit `NO`","quick_reference_pdf":"use the displayed quick-reference PDF title, not the filename","summary_heading":"use the first substantive heading from the final-rule PDF","sorting_or_selection":"final-rule-date ascending"}}