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 replacing large-scale training sets with a small number of synthetic training data points. Search arXiv records from 2019 to 2021 for both the phrases "dataset distillation" and "dataset condensation". Retain only entries whose primary category is Machine Learning; do not include records that are only cross-listed into Machine Learning by other primary-category papers. From these, further select papers that had a version update in the calendar year following their initial submission and whose current arXiv record still clearly indicates a subsequent publication venue. Finally, 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 items 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 a phrase from the \"dataset distillation\" or \"dataset condensation\" family, and the semantics must refer to replacing large-scale training sets with a small number of synthetic training data points.","The current primary category must be cs.LG. Do not include records that are only cross-listed into Machine Learning by other primary-category papers.","The paper must have a version update in the calendar year following its 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 next-calendar-year version 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 record 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 a phrase from the \"dataset distillation\" or \"dataset condensation\" family, and the semantics must refer to replacing large-scale training sets with a small number of synthetic training data points.","The current primary category must be cs.LG. Do not include records that are only cross-listed into Machine Learning by other primary-category papers.","The paper must have a version update in the calendar year following its 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 are only cross-listed to cs.LG.","Exclude records where the title or abstract contains the words \"distillation\" or \"condensation\" but does not correspond to the dataset distillation or dataset condensation research line.","Exclude records that have no next-calendar-year version update, 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 prompts for large language models. I want to find arXiv papers whose first version was submitted in 2022 and that explicitly focus on chain-of-thought or self-consistency reasoning methods. Start with arXiv records first submitted in 2022. First, check whether the title or abstract explicitly contains clues related to chain-of-thought or self-consistency. Then, retain only papers whose primary category is Computation and Language; do not include records that are only cross-listed from other primary categories. Next, verify that these papers actually had a version update in 2023, and confirm that the current arXiv record's comments field clearly indicates subsequent publication information. Finally, output a table sorted in ascending order by the date of each paper's earliest 2023 version update, with columns: arXiv ID, cue family, cue location, v1 submitted, first 2023 version, date of that version, primary category, publication clue. If no qualifying items 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 include arXiv papers whose v1 was submitted in 2022.","The title or abstract must explicitly contain clues 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.","Must have had a version update in 2023, and the current comments field must clearly indicate subsequent publication information."],"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 include arXiv papers whose v1 was submitted in 2022.","The title or abstract must explicitly contain clues 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.","Must have had a version update in 2023, and the current comments field must clearly indicate subsequent publication information."],"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 weakly related hints, but whose title and abstract lack explicit 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 research. Identify arXiv papers whose first version was submitted in Q4 2020 and whose title or abstract explicitly uses vision- or image-domain Transformer terminology. First, filter for papers with a first-submission date in Q4 2020. Then check whether the title or abstract explicitly contains vision- or image-related Transformer expressions. Retain only papers whose primary category is Computer Vision and Pattern Recognition. Next, confirm that the paper had at least one version update in 2021, and check whether the current arXiv record clearly indicates subsequent publication information. Finally, output a table sorted in ascending order by the date of each paper's earliest 2021 version. 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 items exist, output NONE.

Output a table sorted in ascending order by the date of each paper's earliest 2021 version. 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 items 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 submitted date falls in Q4 2020.","The title or abstract must explicitly contain vision- or image-domain Transformer-related expressions.","The current primary category must be cs.CV.","The paper must have had a version update in 2021, and the current record 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","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 submitted date falls in Q4 2020.","The title or abstract must explicitly contain vision- or image-domain Transformer-related expressions.","The current primary category must be cs.CV.","The paper must have had a version update in 2021, and the current record must clearly 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 or ViT, but whose title and abstract lack explicit vision- or image-domain expressions.","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: I am compiling a reading list of certified robustness defense research from the first half of 2020. I want to find arXiv papers whose first version was submitted between January and June 2020 and whose title or abstract explicitly focuses on smoothed classifiers, randomized smoothing and its variants, certified defenses against backdoor or poisoning attacks, or partition aggregation. Start with arXiv records whose first submission date falls between January and June 2020. Check whether the title or abstract explicitly mentions randomized smoothing, de-randomized smoothing, transformation-specific smoothing, smoothed classifiers, certified defenses against backdoor/poisoning, or partition aggregation. Then retain only papers whose primary category is Machine Learning; do not include records that are only cross-listed from other primary categories. Next, confirm that these papers actually had a version update in 2021, and check whether the current arXiv record clearly indicates formal publication or acceptance information. Finally, output a table sorted by the date of each paper's earliest 2021 version in ascending order, 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 whose v1 submission date falls between 2020-01-01 and 2020-06-30.","The title or abstract must explicitly 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.","The paper must have had a version update in 2021, and the current record must clearly indicate formal publication or acceptance information."],"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 whose v1 submission date falls between 2020-01-01 and 2020-06-30.","The title or abstract must explicitly 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.","The paper must have had a version update in 2021, and the current record must clearly indicate formal publication or acceptance information."],"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 that have no version update in 2021 or no 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 counties with limited internet access, a high share of seniors living alone, weak transportation conditions, and low income. Scope the analysis to North Carolina counties from this dataset and use the North Carolina statewide values from the same release as the comparison baseline. First, select counties with at least 4,000 households lacking internet access. Then, retain only counties that simultaneously meet all of the following conditions: share of households without internet access higher than the statewide share; share of households with a householder aged 65 or older living alone higher than the statewide share; share of households without a vehicle higher than the statewide share; and median household income lower than the statewide median. Finally, output all qualifying counties as a single line, sorted alphabetically by county name. Each county should be formatted as County|NoInternetHH|NoInternetRatePct|SeniorLivingAloneSharePct|NoVehicleRatePct|MedianIncome, with counties separated by commas. Percentage fields should be given to three decimal places without a percent sign.

Output all qualifying counties as a single line, sorted alphabetically by county name. Each county should be formatted as County|NoInternetHH|NoInternetRatePct|SeniorLivingAloneSharePct|NoVehicleRatePct|MedianIncome, with counties separated by commas. Percentage fields should be given to three decimal places without a percent sign.

{"State-Gated Retrieval":["Use only North Carolina county data from the 2014-2018 American Community Survey 5-year estimates, and use the North Carolina statewide values from the same release as the comparison baseline.","Retain only counties with at least 4,000 households lacking internet access, and where the share of households without internet access, the share of households with a householder aged 65+ living alone, and the share of households without a vehicle are all higher than the statewide shares, and the median household income is lower than the statewide median.","Output the final results sorted alphabetically 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 North Carolina county data from the 2014-2018 American Community Survey 5-year estimates, and use the North Carolina statewide values from the same release as the comparison baseline.","Retain only counties with at least 4,000 households lacking internet access, and where the share of households without internet access, the share of households with a householder aged 65+ living alone, and the share of households without a vehicle are all higher than the statewide shares, and the median household income is lower than the statewide median.","Output the final results sorted alphabetically 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 other non-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 meet any one of the thresholds or statewide comparison conditions."],"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 have a large renter population, significant language barriers, limited internet access, transportation difficulties, low income, and rental stress. Use the New Mexico county-level data from this release and the New Mexico statewide values from the same release as the comparison baseline. First, retain counties with at least 2,500 renter households and at least 500 limited-English-proficient households. Then, keep only counties that meet all of the following conditions: the share of limited-English-proficient households is higher than the statewide share; the share of households with no internet access is higher than the statewide share; the share of households with no vehicle is higher than the statewide share; and the median household income is lower than the statewide median. Next, evaluate the rental housing stress indicators for renter households. Retain only counties that meet at least one of the following: the share of renter households with a rent-to-income ratio of 35% or more is higher than the statewide share, or the share of renter households with more than one occupant per room is higher than the statewide share. If both conditions are met, indicate both. Finally, output all qualifying counties as a single line, sorted alphabetically by county name. Each county should be formatted as County|RenterHH|LEPHH|LEPSharePct|NoInternetPct|NoVehiclePct|SevereRentBurdenPct|CrowdedRenterPct|MedianIncome|StressPath, with counties separated by commas. StressPath must be RENT, CROWD, or BOTH. Percentage fields should have three decimal places and no percent sign.

Output all qualifying counties as a single line, sorted alphabetically by county name. Each county should be formatted as County|RenterHH|LEPHH|LEPSharePct|NoInternetPct|NoVehiclePct|SevereRentBurdenPct|CrowdedRenterPct|MedianIncome|StressPath, with counties separated by commas. StressPath must be RENT, CROWD, or BOTH. Percentage fields should have three decimal places and no percent sign.

{"State-Gated Retrieval":["Use only the 2015-2019 American Community Survey 5-year estimates for New Mexico counties, and use the New Mexico statewide values from the same release as the comparison baseline.","Candidate counties must have at least 2,500 renter households and at least 500 limited-English-proficient households. Additionally, the LEP share, no-internet share, and no-vehicle share must all be higher than the statewide shares, and the median household income must be lower than the statewide median.","LEPHH must be computed by summing all four limited-English language groups from C16002. Finally, retain only counties where at least one of Severe Rent Burden or Crowded Renter is higher than 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 the 2015-2019 American Community Survey 5-year estimates for New Mexico counties, and use the New Mexico statewide values from the same release as the comparison baseline.","Candidate counties must have at least 2,500 renter households and at least 500 limited-English-proficient households. Additionally, the LEP share, no-internet share, and no-vehicle share must all be higher than the statewide shares, and the median household income must be lower than the statewide median.","LEPHH must be computed by summing all four limited-English language groups from C16002. Finally, retain only counties where at least one of Severe Rent Burden or Crowded Renter is higher than 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 that do not recalculate LEPHH/LEPSharePct/LEPShare_state.","Exclude counties that do not meet 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: I am compiling a pre-pandemic county screening list for the California Department of Education to identify counties where, during annual school meal applications and parent data updates, schools cannot rely solely on English online portals and must still provide paper materials and in-person assistance. Use 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. First, select counties with a population aged 5–17 of at least 75,000. Then, retain counties with at least 2,000 individuals aged 5–17 who speak English "not well" or "not at all" and whose share of the 5–17 population exceeds the statewide share. Next, check these counties' digital access and income conditions: the share of households with no internet access must exceed the statewide share, and the median household income must be below the statewide median. Finally, among three offline support signals, retain counties where at least two exceed the statewide share: the share of households with no vehicle, the share of minors in households that received SSI, cash public assistance, Food Stamps, or SNAP in the past 12 months, and the share of renter-occupied housing units with more than one occupant per room. Output all qualifying counties in a single line, sorted alphabetically by county name. Each county should be formatted as County|SchoolAgePop|SchoolAgeLEP|LEPSharePct|NoInternetPct|NoVehiclePct|ChildBenefitSharePct|CrowdedRenterPct|MedianIncome|SupportPath, with counties separated by commas. SupportPath uses only the three labels BENEFITS, CROWD, and MOBILITY: the public assistance signal corresponds to BENEFITS, the crowded housing signal corresponds to CROWD, and the no-vehicle signal corresponds to MOBILITY. If multiple labels apply, join them with + in the fixed order BENEFITS+CROWD+MOBILITY.

{"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 of at least 2,000 with a share exceeding the statewide share, a no-internet share exceeding the statewide share, and a median household income below the statewide median.","SchoolAgeLEP must be the sum of the eight \"not well\" and \"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) exceeding 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 of at least 2,000 with a share exceeding the statewide share, a no-internet share exceeding the statewide share, and a median household income below the statewide median.","SchoolAgeLEP must be the sum of the eight \"not well\" and \"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) exceeding the statewide share."],"exclusion_conditions":["Exclude results that only take the two Spanish LEP rows and do not combine all eight target cells.","Exclude results that do not re-run the B28011, B19013, B08201, B09010, and B25014 branches.","Exclude counties whose final SupportPath does not have at least two signals exceeding 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 2012–2016 American Community Survey 5-year estimates, identify Pennsylvania counties with high senior evacuation support needs that warrant proactive in-person outreach before winter storms. Use Pennsylvania county-level data from the 2012–2016 American Community Survey 5-year estimates, with Pennsylvania statewide values from the same release as the comparison baseline. First, retain counties with a population aged 65 and over of at least 15,000. Then, among those, keep counties with at least 4,000 people aged 65 and over who have ambulatory difficulty, and whose share exceeds the statewide share. Additionally, the county median household income must be below the statewide median. Next, evaluate four evacuation support barriers: share of households with a householder aged 65+ living alone, share of households with no vehicle, share of renter households with no vehicle, and share of mobile homes among all housing units. Retain only counties where at least two of these barriers exceed the statewide values. Finally, output all qualifying counties in a single line, sorted alphabetically by county name. Each county should be formatted as County|SeniorPop|AmbulatoryDifficulty65Plus|AmbDiffPct|SeniorLivingAloneSharePct|NoVehiclePct|RenterNoVehiclePct|MobileHomePct|MedianIncome|BarrierPath, with counties separated by commas. For BarrierPath, use only the labels ALONE, MOBILE_HOME, NO_VEHICLE, and RENTER_NO_VEHICLE. If multiple labels apply, join them with + in the fixed order ALONE+MOBILE_HOME+NO_VEHICLE+RENTER_NO_VEHICLE.

Output all qualifying counties in a single line, sorted alphabetically by county name. Each county should be formatted as County|SeniorPop|AmbulatoryDifficulty65Plus|AmbDiffPct|SeniorLivingAloneSharePct|NoVehiclePct|RenterNoVehiclePct|MobileHomePct|MedianIncome|BarrierPath, with counties separated by commas. For BarrierPath, use only the labels ALONE, MOBILE_HOME, NO_VEHICLE, and RENTER_NO_VEHICLE. If multiple labels apply, join them with + in the fixed order ALONE+MOBILE_HOME+NO_VEHICLE+RENTER_NO_VEHICLE.

{"State-Gated Retrieval":["Use only Pennsylvania county-level data from the 2012-2016 American Community Survey 5-year estimates, and use Pennsylvania statewide values from the same release as the comparison baseline.","Retain only counties with a population aged 65 and over of at least 15,000, at least 4,000 people aged 65 and over with ambulatory difficulty and a share higher than 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—higher than the statewide values."],"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-level data from the 2012-2016 American Community Survey 5-year estimates, and use Pennsylvania statewide values from the same release as the comparison baseline.","Retain only counties with a population aged 65 and over of at least 15,000, at least 4,000 people aged 65 and over with ambulatory difficulty and a share higher than 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—higher than the statewide values."],"exclusion_conditions":["Exclude results that take only the 75 years and over ambulatory difficulty cells and mistakenly treat them as the full 65+ value.","Exclude results that do not use the Pennsylvania statewide baselines from the same release.","Exclude counties whose final BarrierPath does not satisfy the condition of at least two barriers higher than the statewide values."],"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. 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:
Screen the four South Texas climate divisions (South Central, Upper Coast, South, and Lower Valley) to identify divisions that remained near-normal to wet over two longer periods yet still had low heating demand in March 1966. Restrict the search strictly to these four divisions; use the official Texas climate-division page for their exact names and numbers.

Retain only divisions that meet all three of the following criteria:
- The 10-month Palmer Z-Index anomaly ending February 1966 must be at least -0.05.
- The 24-month Palmer Z-Index anomaly ending December 1966 must be at least -0.10.
- The March 1966 single-month Heating Degree Days actual value must be below 200°Df.

After identifying qualifying divisions, sort them as follows:
- Primary: sum of the February 1966 10-month Z anomaly and the December 1966 24-month Z anomaly, in descending order.
- Secondary: March 1966 Heating Degree Days actual value, in ascending order.
- Tertiary: Texas division number, in ascending order.

Output a single-line string with qualifying entries separated by commas. Each entry must follow this exact format:
<Texas division number>|<division name>|<raw February 1966 10-month Z text>|<raw December 1966 24-month Z text>|<raw March 1966 HDD text>|<moisture score rounded to 2 decimal places>

The moisture score is the sum of the February 1966 10-month Z anomaly and the December 1966 24-month Z anomaly, formatted to two decimal places. If no division qualifies, output NONE.

{"State-Gated Retrieval":["Screen only within the four South Texas divisions defined on the Texas divisional page: South Central, Upper Coast, South, 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":["Screen only within the four South Texas divisions defined on the Texas divisional page: South Central, Upper Coast, South, 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 to reference for shoulder-season operational planning. I am particularly interested in regions that recovered from a dry state to near-normal or wet conditions in the early 20th century and had light heating loads during both shoulder-season windows.

Identify the divisions in NOAA's Climate at a Glance database that meet these criteria. A division is retained only if it satisfies all four hard constraints: the 10-month Palmer Z-Index anomaly ending April 1907 is no higher than -0.30; the 10-month Z-Index anomaly ending April 1923 is no lower than -0.20; the 2-month Heating Degree Days (HDD) actual value ending May 1915 is below 50°Df; and the 5-month HDD actual value ending October 1922 is below 20°Df.

After identifying the qualifying divisions, sort them. First compute each division's "rebound score" as the April 1923 10-month Z-Index anomaly minus the April 1907 10-month Z-Index anomaly. Sort primarily by rebound score in descending order. If scores are tied, place the division with the smaller October 1922 5-month HDD actual value first. If still tied, give priority to the smaller Texas official division number.

Finally, output the results as a single-line string with divisions joined by commas. Each entry must strictly follow this format: "<Texas division number>|<4-digit division ID>|<division name>|<1907-04 10-month Z-Index anomaly raw text>|<1923-04 10-month Z-Index anomaly raw text>|<1915-05 2-month HDD actual value raw text>|<1922-10 5-month HDD actual value raw text>|<rebound score with 2 decimal places>". If no division meets all conditions, output NONE in uppercase.

Output the results as a single-line string with divisions joined by commas. Each entry must strictly follow this format: "<Texas division number>|<4-digit division ID>|<division name>|<1907-04 10-month Z-Index anomaly raw text>|<1923-04 10-month Z-Index anomaly raw text>|<1915-05 2-month HDD actual value raw text>|<1922-10 5-month HDD actual value raw text>|<rebound score with 2 decimal places>". If no division meets all conditions, output NONE in uppercase.

{"State-Gated Retrieval":["Retain only Texas divisions that satisfy all four historical conditions: the 10-month Palmer Z-Index anomaly ending April 1907 is no higher than -0.30; the 10-month anomaly ending April 1923 is no lower than -0.20; the 2-month HDD Value ending May 1915 is less than 50°Df; the 5-month HDD Value ending October 1922 is less than 20°Df.","Rebound score is defined as the April 1923 10-month anomaly minus the April 1907 10-month anomaly.","Sort by rebound score descending, then by 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 ending April 1907 is no higher than -0.30; the 10-month anomaly ending April 1923 is no lower than -0.20; the 2-month HDD Value ending May 1915 is less than 50°Df; the 5-month HDD Value ending October 1922 is less than 20°Df.","Rebound score is defined as the April 1923 10-month anomaly minus the April 1907 10-month anomaly.","Sort by rebound score descending, then by 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 incorrectly read the 1915 or 1922 HDD page as Anomaly instead of Value.","Exclude any division that does not satisfy all 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 European painted products—aniline, cobalt, chromium, 3,3’-dichlorobenzidine, and dibenz[a,h]anthracene—retain only 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, check whether each of the following five dated keywords appears (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). Record the earliest and latest year-month among the hits. Then, separately from the painted-products record mentioned above, select two use-category routes: one from a daily-consumer perspective with priority order Formulation → Article → Occupation, and one from a production-material perspective with priority order Occupation → Article → Formulation. After selecting each category, drill down along the child category with the highest product count until no further subdivision is possible; if there is a tie at the same level, choose the one with the smaller PUC ID.

Finally, sort results by hit count descending; for ties, sort by the consumer-side terminal node’s product count descending. 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>. Separate results with commas; if no qualifying items exist, output NONE.

{"State-Gated Retrieval":["Screen only the five substances specified in the task, and each target substance page must explicitly contain the record “Arts and crafts/Office supplies - body paint - tattoo ink, detected, Europe.”","Check whether each of the five specified dated keywords appears, and record the earliest and latest year-month among the hits.","Then independently select one daily-consumer route and one production-material 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":"current ChemExpo chemical pages and PUC category paths for the five named substances, using the dated keyword states through 2023-01 and the current Europe body-paint/tattoo-ink record"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["Screen only the five substances specified in the task, and each target substance page must explicitly contain the record “Arts and crafts/Office supplies - body paint - tattoo ink, detected, Europe.”","Check whether each of the five specified dated keywords appears, and record the earliest and latest year-month among the hits.","Then independently select one daily-consumer route and one production-material 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 body-paint/tattoo-ink record.","Exclude results that directly reuse the same daily-consumer 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, I want to check whether each of these six substances currently appears more often in material-contact-related uses or in manufacturing-source-related uses, and then select the most representative use category from the corresponding direction. Follow the steps below. First, restrict the scope to these six substances: Di(2-Ethylhexyl) Phthalate, Melamine, Tris(2-Ethylhexyl) Phosphate, 4,5-Dihydro-2-Mercaptoimidazole, Phthalic Acid, Triphenyl Phosphates Tert-Butylated. Then, for each substance, first check whether any of the five material-contact-related label categories appear; score 1 point for each category that appears: Indirect additives food contact (10/2018); Europe, Food contact items; Food contact items, Japan; any keyword containing "Toys and children's products"; any keyword containing "artificial_saliva" or "artificial_sweat". Next, check whether any of the two manufacturing-source-related label categories appear; score 1 point for each category that appears: drinking_water, Europe, manufacturing, plastic_additive; Europe, plastic_additive. Additionally, separately count how many of the following four dated keywords appear: 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 determine the direction based on which side has a higher score: if the material-contact score is strictly greater than the manufacturing-source score, search for the most representative category for this substance in the order Article → Formulation → Occupation; otherwise, search in the order Occupation → Article → Formulation. After determining the category, from this level onward, at each level only follow the subcategory with the highest product count, continuing until no further subdivision is possible; if there is a tie at the same level, choose the one with the smaller PUC ID.

Finally, sort all substances by dated hit count descending; for ties, sort by product count of the selected node descending. Output format: <DTXSID>|<contactHits>|<sourceHits>|<datedHits>|<earliest>|<latest>|<prioritySide>|<PUCkind>|<generalPUCID>|<terminalPUCID>|<level>|<Products>|<PUCDocuments>|<PUCCuratedChemicals>. Separate results with 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 first be determined by these historical signals, and then a most representative current use category is selected along the corresponding direction.","The current route selection must follow the given kind priority and the same-level smaller PUC ID tie-break rule for further drill-down."],"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":"current ChemExpo chemical pages and PUC category paths for the six named substances, using the stated historical keyword families and dated hits through 2023-01"},"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 first be determined by these historical signals, and then a most representative current use category is selected along the corresponding direction.","The current route selection must follow the given kind priority and the same-level smaller PUC ID tie-break rule for further drill-down."],"exclusion_conditions":["Exclude results that ignore historical signals and directly select the path with the maximum current visible Products on the chemical page.","Exclude results that start PUC route selection without first determining the contact/source direction.","Exclude results that do not apply the smaller PUC ID tie-break rule for same-level ties."],"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: In ChemExpo, first select Formulation categories with a total product count of 15,000 or more. Then, within each category, following the website's default display order, identify the first product family that meets these criteria: the family itself has no assumed attributes and its total product count is at least 3,000; additionally, the family must have at least two distinct specific product types, one with allowed attributes including aerosol and the other including child, and both types must have empty assumed attributes. Next, within each of these two specific product types, compute the product share (type product count divided by family cumulative product count). If shares are equal, compare their Curated Chemicals counts and select the higher one; if still equal, select the type with the smaller PUC ID. After completing the selection on both sides, retain only the better of the two candidate types: first prioritize the type with the higher product share; if shares are equal, compare Product Count (higher wins); if still equal, compare Curated Chemicals (higher wins); if still equal, select the type with the smaller PUC ID.

Output results sorted by final share in descending order, formatted as: <generalPUCID>|<familyPUCID>|<typePUCID>|<side>|<share>|<CuratedChemicals>. Join records with commas and no spaces. If no qualifying items exist, output NONE.

{"State-Gated Retrieval":["First, retain only Formulation categories with a total product count of 15,000 or more.","Within 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 empty assumed attributes.","All final selections within a family and between the two sides must follow the specified tie-break rules: share, Product Count, Curated Chemicals, and 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":"Current ChemExpo Formulation families with total product count >= 15,000, evaluated on family/type attributes and current product/chemical counts"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["First, retain only Formulation categories with a total product count of 15,000 or more.","Within 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 empty assumed attributes.","All final selections within a family and between the two sides must follow the specified tie-break rules: share, Product Count, Curated Chemicals, and 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 a family and overall."],"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 align with their current uses. Follow the steps below. First, restrict the scope to these five substances. For each substance, collect three sets of baseline assessment data. The first set is the "material contact" score: check for the presence of the following five label categories, scoring 1 point for each category hit: 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". The second set is the "production source" score: check for the presence of the following two label categories, scoring 1 point for each category hit: "drinking_water, Europe, manufacturing, plastic_additive" and "Europe, plastic_additive". The third set is the regulatory profile for specific dates: separately count the total hits for the four exact keywords Indirect additives food contact (10/2018), OEHHA Proposition 65 (3/2019), WA Children's Safe Product Act (4/2020), and OEHHA Proposition 65 (1/2023), and record the earliest and latest year-month among all hits. After obtaining these three sets of data, determine the historical side by comparing scores: if the material contact score is strictly greater than the production source score, select a historically recommended kind in the order Article → Formulation → Occupation; otherwise, select in the order Occupation → Article → Formulation. After determining the kind, drill down along the subcategory with the most products at each level until no further subdivision is possible; if there is a tie at the same level, choose the one with the smaller PUC ID. Separately, independently identify the current dominant category for the same substance, ignoring historical clues: directly find the path with the most products among all use categories, drilling down to the terminal level using the same tie-breaking rule (smaller PUC ID). Finally, compare the two results: if the current dominant kind is Article or Formulation and the historical side is material, mark ALIGNED; if the current dominant kind is Occupation and the historical side is source, also mark ALIGNED; otherwise, mark MISALIGNED.

Output MISALIGNED records first; within the same group, sort by datedHits descending; if datedHits are equal, sort by currentProducts 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 datedHits 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":["Operate only on the five specified substances, and first compute materialHits, sourceHits, and datedHits from the keyword-set block.","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 historically recommended category and the current dominant category align at the coarse side level, 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":"current ChemExpo chemical pages and PUC category paths for the five named substances, using the specified historical keyword-set evidence through 2023-01"},"answer_type":"multi-row ordered table"}

{"inclusion_conditions":["Operate only on the five specified substances, and first compute materialHits, sourceHits, and datedHits from the keyword-set block.","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 historically recommended category and the current dominant category align at the coarse side level, and mark ALIGNED or MISALIGNED accordingly."],"exclusion_conditions":["Exclude results that simply take the current largest category and treat it as the historically recommended category.","Exclude results that default to ALIGNED without independently deriving the historically recommended category.","Exclude results that fail to compute the historical and current paths separately, instead mixing signals in a single path."],"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:
On the CFPB enforcement actions page, for the period 2022 to 2024, find one qualifying administrative enforcement action for each of the three product categories: Deposits, Furnishing, and Prepaid. Then, in the corresponding Consent Order for that action, locate the first occurrence of the consumer redress amount and the civil money penalty amount, along with their page numbers.

Specifically, first restrict the search to actions on the CFPB enforcement actions page whose Initial filing date falls between 2022-01-01 and 2024-12-31.

Then, search separately for each of the three product categories: Deposits, Furnishing, and Prepaid. For each category, follow the current default case order on the CFPB website from top to bottom, and select the first action that simultaneously meets all of the following conditions:
1. The action page provides both a Consent Order and a press release.
2. The product category label corresponds one-to-one with the target category.
3. The page explicitly states consumer redress, and the redress amount exceeds $5,000,000.
4. In addition to consumer redress, the page also explicitly states a civil money penalty.

After finding such an action for each product category, open its corresponding Consent Order and locate the two types of monetary information:
- The specific amount of consumer redress when it first appears in the text, and the page number where it appears.
- The specific amount of civil money penalty when it first appears in the text, and the page number where it appears.

Page numbers should be based on the physical page numbering of the Consent Order PDF, i.e., the first page is p1, the second page is p2, and so on.

Finally, output the results for the three product categories sorted alphabetically by product name. Output one segment per category in the following format:
<Product>|<Respondent>|<Docket>|<FilingDate>|<RedressUSD>|p<RedressPage>|<PenaltyUSD>|p<PenaltyPage>
If no qualifying case is found for a category, output:
<Product>|NONE
Join the three results with commas, with no spaces.

{"State-Gated Retrieval":["Only consider 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 in the current default order from top to bottom 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; the action detail page explicitly states consumer redress > $5,000,000 and also states a civil money penalty.","Finally, extract the first-occurrence amounts and page numbers of consumer redress and civil money penalty from the correct Consent Order."],"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/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":["Only consider 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 in the current default order from top to bottom 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; the action detail page explicitly states consumer redress > $5,000,000 and also states a civil money penalty.","Finally, extract the first-occurrence amounts and page numbers of consumer redress and civil money penalty from the correct Consent Order."],"exclusion_conditions":["Exclude actions where the Products label is not a single target category, or where the action appears relevant only at the list level but the detail page does not confirm it.","Exclude actions that do not clearly satisfy the redress > $5M and independent civil money penalty conditions.","Exclude cases where a Stipulation or Order Terminating the Consent Order is mistakenly used in place of the required Consent Order."],"normalization":{"field_separator":"|","record_separator":",","schema":["Product","Respondent","Docket","FilingDate","RedressUSD","RedressPage","PenaltyUSD","PenaltyPage"],"product_order":"Alphabetical by Product name","fallback_row":"If a product has no qualifying case, emit `<Product>|NONE`","date_format":"FilingDate uses YYYY-MM-DD","money_fields":"Canonical output preserves the dollar sign and commas from the oracle, but equivalent comparison may ignore `$` 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 report series in CFPB Reports, whose latest version page still links directly to the 2023 and 2022 editions. Some topics that appeared in the 2022 edition were later dropped from subsequent annual reports, and I want to see which other reports on the site are associated with these dropped topics. First, locate this series within the CFPB Reports section and confirm that the latest version links directly to the two previous editions (2023 and 2022). Then, using the 2022 edition as the baseline, identify topics that appear in the 2022 edition but are no longer retained in subsequent annual reports. Note: exclude the topic specifically used to mark "belongs to the same survey series." Next, for each identified topic, confirm the following one by one: first, the year in which this topic first disappeared from subsequent annual reports; second, when searching for this topic within the CFPB Reports section, among results from 2022 and earlier, which report that does not belong to this series appears first; third, open that report, count how many related resources are listed on the page, how many of those explicitly link to the same survey series, and among those reports that link to the same survey series, how many have appendices labeled alphabetically with at least three sections (e.g., Appendix A, B, C or more); fourth, for the report that survives this multi-level screening, provide its title, publication date, and the alphabetical range of its appendices.

Finally, output a single-line string 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, locate the CFPB household financial well-being annual report series whose latest version links directly to 2023 and 2022.","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, locate the CFPB household financial well-being annual report series whose latest version links directly to 2023 and 2022.","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 from 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 sort out the CFPB's 2020 Debt Collection Rules, including proposal sources, delayed effective date arrangements, final effective dates, and the accompanying quick-reference PDF information on the implementation page. Follow the steps below. First, confirm when each of the two 2020 Debt Collection Rule final rules was issued. Then separately confirm the following for each wave: 1. The issue date of this final rule; 2. Which proposal(s) it developed from, and the public comment deadline for each proposal; 3. Whether the 2021 proposed rule on delaying the effective date covers this wave, and the comment deadline for that proposal; 4. The actual final effective date; 5. Which quick-reference PDF corresponds to this wave on the implementation page; 6. The first heading in the PDF body after skipping the introductory paragraph.

Output a single-line string with results sorted by final-rule issue date in ascending order. Identify each wave by its final rule issue month (YYYY-MM). Use the following format: <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":["Must process the two 2020 Debt Collection Practices (Regulation F) final rules separately, not merge them into one.","For each wave, separately confirm: issue date, corresponding proposal chain and public comment deadlines, whether the 2021 delayed-effective-date proposed rule covers it and its deadline, final effective date, corresponding implementation quick-reference PDF, and the first heading after the introduction in that PDF.","The detail page, proposal chain, and summary PDF for the October wave and the December wave must each be independently verified."],"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":["Must process the two 2020 Debt Collection Practices (Regulation F) final rules separately, not merge them into one.","For each wave, separately confirm: issue date, corresponding proposal chain and public comment deadlines, whether the 2021 delayed-effective-date proposed rule covers it and its deadline, final effective date, corresponding implementation quick-reference PDF, and the first heading after the introduction in that PDF.","The detail page, proposal chain, and summary PDF for the October wave and the December wave must each be independently verified."],"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 do not independently verify each final-rule detail page and its 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"}}