Canaries Dashboard

register entry canaries-in-the-coal-mine-aio-20261029 OBS-8527c06bd274 · file as first run sha256 b4e101353182a340…

• T1-1 definition (the idiom): 'A "canary in a coal mine" is an early warning sign or indicator of hidden danger, trouble, or failure.' (two markers)
• T1-2 toxic gas detection: 'In the late 19th and 20th centuries, coal miners brought caged canaries into underground tunnels.' (two markers)
• T1-3 physiology: 'Canaries have a rapid breathing rate and high metabolism, making them much more sensitive to lethal, odorless gases like carbon monoxide and methane than humans.' (two markers)
• T1-4 the warning signal: 'As long as the bird sang and acted normally, the air was safe. If the canary grew distressed, fell silent, or collapsed, it signaled that poisonous gas was building up and miners needed to escape.' (three markers)
• T1-5 replacement: 'The practice continued until electronic gas detectors replaced live birds in the late 20th century (around 1996).' (two markers)
• T1-6 modern idiom: 'Today, the phrase describes anything that breaks down or shows negative changes first during a larger crisis' (two markers)
• T1-7 economics: 'Declining hotel occupancy or airport traffic used as an early sign of an incoming economic downturn.' (one marker)
• T1-8 environment: 'Melting glaciers, dying coral reefs, or disappearing insects used as early warnings for climate change and ecosystem stress.' (two markers)
• T1-9 technology & labor (the entity): 'Studies such as the Stanford Digital Economy Lab report tracking entry-level or young worker employment shifts as early indicators of artificial intelligence impacts on the job market.' (the Lab's name an inline link; one marker)
• T1-10 closer: 'Would you like to explore how this idiom is applied to a specific industry, environmental issue, or economic trend?'

• B1 Stanford Digital Economy Lab — Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... card; B1.html sha256 352ff6c8dee78a64, HTTP 200 at 2026-10-29T22:31:26Z; byte-identical to the census copy stanford-canaries--paper; PDF sha256 c8d2e5c4ccc0de7e, HTTP 200 at 2026-10-29T22:31:51Z
• B2 The Gale Review — The historic roots and modern connotations of "Canary in the Coal Mine" homonym's card, fetched for its sense; B2.html sha256 31b27ebe8bf60bd4, HTTP 200 at 2026-10-29T22:33:19Z; not ledgered
• B3 Reddit·r/AskHistorians — Why were canaries used in coal mines? : r/AskHistorians - Reddit homonym's card; unreachable: no thread URL found; Reddit search page HTTP 200, a bot check (B3-try1.html sha256 351ed5d5bc7c40f2); not retried; title and snippet only
• B4 YouTube·iswearenglish — Canary in the Coalmine Definition homonym's card (video): title only; not fetched
• B5 YouTube·Beamish Museum — Why were canaries used down the mine? homonym's card (video): title only; not fetched
• B6 Cambridge Dictionary — Meaning of canary in a coalmine in English - Cambridge Dictionary homonym's card, fetched for its sense; B6.html sha256 6f7032f23435ec1d, HTTP 200 at 2026-10-29T22:33:19Z; not ledgered
• B7 Government of Alberta — Canaries in the Coal Mine - Coal - Alberta's Energy Heritage homonym's card, fetched for its sense; B7.html sha256 01f43ec0cede9b00, HTTP 200 at 2026-10-29T22:33:20Z; not ledgered
• B8 YouTube·Tutor Nick P — Canary in a Coalmine - Idioms (770) Origin - English Tutor Nick P homonym's card (video): title only; not fetched
• B9 YouTube·Smartest Year Ever — Canaries in Coal Mines 🐤 #dailyfacts homonym's card (video): title only; not fetched
• B10 Facebook·Amazing Time Capsule — How Canaries Saved the Lives of Miners | Amazing Time Capsule homonym's card (video): title only; not fetched
• B11 Stanford Digital Economy Lab — Canaries Dashboard (AI Economic Indicators) own_page; census copy stanford-canaries--dashboard (read 2026-10-29T21:49:16Z, HTTP 200); B11.html sha256 2db132b18f4da51b
• B12 Wikipedia — Canaries in the Coal Mine? / Canaries Dashboard (no article; cited in three articles) no article: raw HTTP 404 under every name, rendered HTTP 404; the idiom redirects to 'Sentinel species#Canaries'; cited in three articles; B12.html sha256 8c8551f5d3203b6a

E_A, the archive's entity: the same paper and dashboard, in the Labor Vector census record and the archive's deposit: a working paper and monthly-updated dashboard of observed employment outcomes, on the intersection of the distribution of gains and losses, whose labor object is the employment of workers in ADP firms grouped by age and occupational exposure, supplied through the payroll records, with who directs the hiring and who receives the gains not recorded and its costs recorded (census record); its automation-versus-augmentation grouping, from the Anthropic Economic Index, read as where a usage measure of who directs the task meets an observed employment outcome (census reading); a member of the class of research on AI's labor displacement, which measures which jobs AI eliminates, beside which the archive sets a channel where labor remains and value tied to provenance is extracted, compositional authority transfer (#716); whose composition at its bare name gives the idiom and carries the study as one instance of it (Capture Registry, 2026-10-29) LV-01 LV-03 LV-05 LV-06 LV-08 LV-09 LV-10 LV-11 LV-23 LV-24 LV-30 O716-01 O716-02 CAP-1-02 CAP-1-05 CAP-1-07; defining papers #716

E_F, the field's entity (type C — a conventional reading holds it (public entity: a working paper and its monthly dashboard, observing employment outcomes; at the bare name a homonym, the idiom)): the working paper 'Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence' by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen of the Stanford Digital Economy Lab (first version August 2025, revised 12 August 2026; B1), which uses ADP administrative payroll data on millions of U.S. workers to document six facts (B1): no widespread, economy-wide job displacement; employment of workers aged 22–25 in AI-exposed occupations 19% below where it would be had it kept pace with less-exposed peers, experienced workers showing no comparable gap; widening since August 2025; through reduced hiring; concentrated where AI usage substitutes for tasks; through employment rather than base compensation; read by its authors as 'early, descriptive indicators—canaries in the coal mine—rather than causal estimates' (B1); and the Canaries Dashboard of the Lab and ADP Research ('AI Economic Indicators: Canaries Dashboard', B11), which updates and extends it monthly on a balanced sample of ADP firms, matched to exposure from Eloundou et al. (2024) and the Anthropic Economic Index, and measures correlation, not a causal link (B11). Wikipedia has no article on it; three articles cite it (B12). The cards at the bare name other than card 1 are the idiom's, a distinct entity.

ρ = extends: The archive reads the Canaries Dashboard as its authors present it and adds, on the field's own object: the census's answer to who supplies, who directs and who gains, with direction and gains recorded as not recorded and costs as recorded; the census's reading of the automation ratio as a usage measure of who directs the task; a channel the employment count does not register, extraction without attribution where labor remains (#716); and the registry's account of the composition at the bare name, which gives the idiom and keeps the study as its instance. LV-10 LV-11 LV-23 LV-24 LV-30 O716-01 O716-02 CAP-1-02 CAP-1-05 CAP-1-07 CAP-1-13. Guard: ρ = extends claims an addition and no more (§1.2a). It does not make the field's sources wrong at their dates. The census's reading of direction (LV-24) is composed beside the dashboard's own hypothesis (F42) and its limit (F47), not in their place; #716's channel stands beside the class's channels and is not a reading of the entity's findings. The homonym, the idiom, stands outside ρ: a distinct entity, never a sense of the name.

The case: equal terms, ontology included (v0.9). The archive reads the field's entity as the entity. The Labor Vector census record describes the Canaries Dashboard and its paper from the Lab's own pages, each field with its basis ('operator'), and answers for it the census's organizing question of who supplies the labor, who directs it and who receives its gains, with what kind of evidence; the capture record checks the composition at the bare name against those pages (CAP-1-01–CAP-1-04). No deposit names the dashboard, the paper or its authors. One deposit bears on its class, research on the employment effects of AI, and adds a channel on the field's own object: where labor remains, the value tied to provenance is extracted, 'not displacement' but 'extraction without attribution' (#716), a fourth channel beside labor displacement, capability projection and welfare. The census's own reading (lv_intersection), that the automation ratio is where a usage measure of who directs the task meets an observed employment outcome, takes the resource's correlation as given and adds a sense of direction. No defining record contests the field's ontology of the entity or supplies one the field lacks: ρ = extends, the equal-terms case (§0.9, §1.2a). The homonym at the bare name, the idiom, is a distinct entity: the archive's record reads it as the paper's own figure turned outward (CAP-1-13), not as a rival ontology of the entity.

#716 §2.2 Economic Framing of AI Composition: PER identifies a distinct channel: even when human labor *remains* (the author's work is used), the economic value tied to provenance — reputation, traffic, citation credit, contractual rights — is extracted by the system. This is not displacement; it is extraction without attribution.

#716 §5.1 Attribution as Economic Value: Existing AI-economics research addresses three channels: labor displacement, capability projection, and welfare estimation. PER addresses a fourth: **compositional authority transfer**

#LV census.json organizing_question: Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?

#LV census.json rows[slug == 'stanford-canaries'].lv_intersection: none stated. The archive's reading: the dashboard's automation-versus-augmentation grouping, borrowed from the Anthropic Economic Index, is where a usage measure of who directs the task meets an observed employment outcome; the resource itself reads it as correlation.

#capture-registry canaries-in-the-coal-mine-aio-20261029, per_dim.read.notes: The record's own title borrows the idiom, so the generic sense is the target's own frame turned outward; this is recorded here, and not as an inversion, since the idiom is not carried to devalue the study.

equal-terms case: every field claim composed as received, beside the archive claims it stands with. Reading the received column recovers the field (§5.9).

v0.9 §4.10: KO_B sentences surviving in the KO at similarity ≥ 0.95, with positions (computed as apply_v09.py does: difflib.SequenceMatcher ratio, the best KO sentence for each KO_B sentence): v0.9 0 of 15 (positions [None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]; composed first under v0.9, so there is no v0.7 object to measure against). Opening-run check (§5.10): no breach.

"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence" is a working paper by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen of the Stanford Digital Economy Lab, first released in August 2025 and revised on 12 August 2026 (the paper's page; the paper). Using "high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026", it documents six facts about the labor market after generative AI, addressing "timeliness, worker granularity, and administrative verifiability" on a balanced panel of 3.5 to 5 million employees a month (the paper). It finds no evidence of widespread, economy-wide job displacement, but employment of workers aged 22 to 25 in AI-exposed occupations stands 19% below where it would be had it kept pace with less-exposed peers, with no comparable gap for experienced workers; in levels, about 11% down against about 10% up (the paper). The divergence has widened since August 2025, runs through reduced hiring rather than separations, and adjusts through employment rather than base compensation (the paper). Declines concentrate where AI usage substitutes for human tasks and employment is flat or rising where it complements workers (the paper); on the dashboard the automation ratio correlates with employment trends where the complementarity share does not clearly, and "the character of AI usage could shape the labor market effects of AI" (the dashboard). The paper reads suggestive evidence of mechanisms in codified knowledge, offers as one possibility that AI substitutes for codified and complements tacit knowledge, and sees early suggestions of an effect on the career ladder (the paper). Women face greater exposure, and early-career women in the most-exposed quintile contract 4.5% a year against 2.5% for men, a difference driven primarily by occupational composition; early-career software developers and customer service workers decline while young home health aides grow (the paper; the dashboard). The divergence survives exclusions of technology firms and computer occupations, interest-rate and remote-work controls and other exposure measures, and allowing for firm entry and exit; with the broadest controls the declines become significant in 2024 (the paper; the dashboard). The authors read their facts "as early, descriptive indicators—canaries in the coal mine—rather than causal estimates", note attenuation under education controls, earlier divergence and stronger patterns in ADP than in national benchmarks, make no predictions, and caution that the work "does not estimate a causal impact of AI" (the paper). The title takes up the paper's premise that generative AI's proficiency with codified knowledge work suggests "canaries in the coal mine" affected first, and Chandar's page describes the work as tracking them for entry-level employment changes (the paper; the paper's page). The Canaries Dashboard, "AI Economic Indicators: Canaries Dashboard" of the Stanford Digital Economy Lab and ADP Research, updates and extends the paper with live trends for a sample of workers in ADP firms, on the premise that specific segments of the labor market can lead the broader economy, and calls itself "an early signal of changes to the labor market, a canary in the coal mine" (the dashboard). It uses a balanced sample of firms with five years of ADP history rolled forward monthly, at launch 25,000 firms and 4.6 million matched workers across over 730 occupations, and matches exposure from Eloundou et al. (2024) and the Anthropic Economic Index, whose terms set full delegation (automation) against collaboration (augmentation) (the dashboard). It measures correlation and not a causal link, on a sample representative neither of the U.S. labor market nor of all ADP firms and closed to firm entry and exit, with possible selection in which firms use ADP (the dashboard; the paper). Updated monthly, last on 21 October 2026, it announces an expanded and reweighted sample; the paper's earlier versions headlined regression estimates of 13% and 16% before the descriptive 19%, a February 2026 update answered the response, the August revision used a larger set of data, and earlier versions are available by email (the dashboard; the paper; the paper's page). Wikipedia has no article on the paper or the dashboard and resolves the idiom to "Sentinel species"; "AI takeover", "Artificial intelligence" and "Silicon Valley" cite the paper, the first with the 2025 figure of 13%, the last against "older, less exposed workers" (Wikipedia).

The Canaries Dashboard is an observed-employment-outcome resource on the distribution of gains and losses, a working paper and monthly-updated dashboard among the Stanford Digital Economy Lab's AI Economic Indicators (Labor Vector census record), which in its own words summarizes, in collaboration with ADP Research, "live labor market trends for a sample workers in firms using ADP payroll services" (the dashboard). The dashboard updates and extends the working paper "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence" by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen (the dashboard; the paper's page), first released in August 2025 and revised on 12 August 2026 (the paper; census record), and the census lists the paper's title and the bare phrase among the entity's names (census record). The name is the idiom put to work: the authors write that generative AI's "proficiency with codified knowledge work" suggests "there may be “canaries in the coal mine” which will be affected first" (the paper), and the dashboard calls its granular, timely results "an early signal of changes to the labor market, a canary in the coal mine", complementing rather than substituting for analyses of nationally representative data (the dashboard). On the dashboard's premise, "specific segments of the labor market can provide a leading indicator of employment trends for the broader economy" (the dashboard), and Bharat Chandar's page describes the work as "tracking “canaries in the coal mine” for entry-level employment changes in jobs exposed to AI" (the paper's page). The bare phrase is therefore a homonym of the study by construction, the paper's question mark marking its title as a use of the idiom, and the phrase is an idiom before it is a title (Capture Registry, 2026-10-29); the name is also the idiom's (Capture Registry), and Wikipedia resolves the idiom to its article "Sentinel species", section "Canaries", not to the study (Wikipedia). Its operator is the Stanford Digital Economy Lab with ADP Research, the paper by Brynjolfsson, Chandar and Chen (census record), and the dashboard asks to be cited as the Stanford Digital Economy Lab and ADP Research's "AI Economic Indicators: Canaries Dashboard" (the dashboard). The paper uses "high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026" to document six facts about the labor market after the adoption of generative AI, and balances "timeliness, worker granularity, and administrative verifiability"; ADP serves firms employing over 26 million U.S. workers, and the analysis panel holds between 3.5 and 5 million employees a month (the paper). The dashboard adopts the paper's balanced sample, firms with five years of employment history in ADP, rolled forward monthly and limited to workers matched to an occupation code: at launch 25,000 firms, 4.6 million matched workers in November 2022 and over 730 occupations (the dashboard; census record), employment indexed from November 2022 and descriptive indices beside event-study regressions (census record). Whose labor it observes the census gives as the employment of these workers grouped by age and by their occupation's AI exposure, the labor supplied by "workers in firms using ADP payroll services (the payroll records)" (census record). Exposure is matched from "Eloundou et al. (2024) and the Anthropic Economic Index" (the dashboard), so the dashboard is not itself a usage measure (census record) and takes from the index its augmentation and automation ratios by occupation (census record); in the index's terms as the dashboard gives them, "users fully delegate tasks" under automation, while augmentation "features collaboration between humans and an AI system" (the dashboard). The paper finds "no evidence of widespread, economy-wide job displacement" (the paper; census record). Its headline is that employment of workers aged 22 to 25 in AI-exposed occupations "now stands 19% below where it would be had it kept pace with that of their less-exposed peers", experienced workers showing "no comparable gap" (the paper; census record); in levels, that age group fell about 11% in the two most exposed quintiles from November 2022 to June 2026 and grew about 10% in the three least exposed (the paper). The divergence "has widened steadily since we first documented it in August 2025", operates "primarily through reduced hiring of young workers rather than increased separations", and adjusts "through employment rather than base compensation" (the paper). The census reads this as a record of costs, the shortfall of young workers' employment in exposed occupations with adjustment through employment, and records who directs the hiring as not recorded and who receives the gains as not recorded, which the evidence cannot settle (census record). Declines concentrate "in occupations where AI usage primarily substitutes for human tasks", while where usage complements workers employment is "flat or rising, especially for experienced workers" (the paper; census record); on the dashboard the complementarity share shows no clear monotonic relationship with employment trends, while the automation ratio "shows a clear correlation with employment trends" (the dashboard; census record). The dashboard draws from this that "the character of AI usage could shape the labor market effects of AI" (the dashboard), and the census reads the same grouping, borrowed from the Anthropic Economic Index, as "where a usage measure of who directs the task meets an observed employment outcome", which the resource itself reads as correlation (census record). The archive's Provenance Erasure Rate paper names the channels AI-economics research measures, labor displacement among them, as frameworks that "measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates", and sets beside them a channel where human labor remains and the value tied to provenance is extracted: "This is not displacement; it is extraction without attribution" (#716). It names that fourth channel compositional authority transfer, "the rate at which AI synthesis captures value from human authors by stripping the attribution that would otherwise carry economic weight" (#716). The paper offers "suggestive evidence about mechanisms", declines for young workers in occupations that involve codified knowledge, and as "One possibility" that AI substitutes for codified knowledge while complementing the tacit knowledge of senior employees; the entry-level hiring slowdown "provides early suggestions that generative AI may be affecting the career ladder" (the paper). Women face greater AI exposure on average (the paper); early-career women in the most-exposed quintile have seen employment contract 4.5% a year since November 2022 against 2.5% for men, a difference the dashboard finds "driven primarily by occupational composition" (the dashboard, updated 22 July 2026; census record), and early-career software developers and customer service workers show substantial declines while home health aides grow for the youngest (the dashboard). The divergence persists when excluding technology firms and computer occupations, when controlling for interest-rate exposure and remote work, and across alternative exposure measures, and is robust to allowing firm entry and exit (the paper); with the broadest set of controls, declines in exposed occupations become statistically significant in 2024, the earlier declines "likely driven by non-AI factors" (the dashboard, from the February 2026 update). The authors interpret the facts "as early, descriptive indicators—canaries in the coal mine—rather than causal estimates" and caution that the work "does not estimate a causal impact of AI"; the patterns attenuate with education controls, show some divergence predating generative AI and are more pronounced in the ADP sample than in national survey benchmarks, and the authors make no predictions (the paper). The dashboard "measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two" (the dashboard), and the census carries the limit with what the evidence cannot settle: "whether AI exposure causes the employment change" (census record). Its sample is "large, but not necessarily representative" and "represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services", the balanced sample "shuts down the margins of firm entry and exit" (the dashboard; census record), and there may be selection in which firms use ADP (the paper). Earlier versions headlined regression estimates, a 13% relative decline at July 2025 data and 16% at September 2025 data, and the August 2026 revision emphasizes the descriptive divergence, 15% at the July 2025 vintage and 19% as of June 2026 (the paper), after a February 2026 update addressing the response to the paper and on a larger set of data (the paper's page). The dashboard was "Last updated October 21, 2026" and is updated monthly, with "an expanded and reweighted sample" announced (the dashboard; census record); earlier versions of the paper are available by email (the paper's page; census record), its license is not stated in the saved text, and the census left the PDF, the chart data, the authors' notes and the license unchecked (census record). The census asks of it its organizing question, "Who supplies the labor, who directs it, and who receives its gains — and what kind of evidence does this resource bring to that question?", and places it in two worked questions, as observed employment change set against exposure estimates and as costs observed where no record measures the distribution of productivity gains (census record). At the bare name on 29 October 2026, Google's AI Overview composed the idiom and carried the study, the Lab's Canaries Dashboard with ADP Research, in one bullet, "at the grain of its subject (young workers' employment as an early indicator)": the Lab named, no author, no identifier, the paper's page cited as card 1, with "early indicators" the record's own frame and no causal or displacement claim made (Capture Registry, 2026-10-29). The finding, the dashboard and its limits are absent; "An idiom outranks the study named after it, which survives as an example of the idiom's use"; the generic sense is the target's own frame turned outward and is not carried to devalue the study; the registry reads that the record would be reached at its title with its question mark or at the Lab's name; and no card cites the census record (Capture Registry, 2026-10-29). Wikipedia has no article on the paper, the dashboard or the Lab; three articles cite the paper, "AI takeover" with the August 2025 version's 13% relative decline, "Artificial intelligence" with decreasing employment rates for early-career workers, and "Silicon Valley" with the 19% gap, which it sets against "older, less exposed workers" where the paper compares less-exposed peers (Wikipedia; the paper).

T against L_F(B) (AIO's ontology at work on its own cards): At 'Canaries in the Coal Mine' (T1, ten cards; its words counted in objects.T) the composition gives the idiom, and the entity once. Substituted reading: the composition's entity is the idiom 'canary in a coal mine' (T1-1, 'an early warning sign or indicator of hidden danger, trouble, or failure'), its mining history (T1-2–T1-5) and its modern uses (T1-6–T1-8), from the homonym's cards (B2–B10; delta.homonym_alignment); of E_F, T composes one bullet, T1-9, 'Studies such as the Stanford Digital Economy Lab report tracking entry-level or young worker employment shifts as early indicators of artificial intelligence impacts on the job market', under the head 'Technology & Labor' and beside hotel occupancy and melting glaciers as instances of the idiom. Composed from card 1 (B1), at a coarse grain: the Lab as maker (F3, without the authors F2: CAP-1-11), the subject at Chandar's grain ('tracking “canaries in the coal mine” for entry-level employment changes in jobs exposed to AI', F14), and the frame 'early indicators' (F13). Limit dropped: F13's own qualification, 'descriptive' and 'rather than causal estimates', and the paper's caution that the work 'does not estimate a causal impact of AI' (F30): T makes no causal claim (CAP-1-12) and states no limit. Available, not composed (card 1's own page and its PDF): the title (F1; the composition's 'canary in a coal mine' is the idiom, singular), the authors (F2), ADP payroll data (F4, F27), all six facts (F5–F10), among them the headline, the 19% gap (F6: CAP-1-03), the robustness and qualifications (F11, F12, F29), the mechanisms (F22–F24), the gender exposure (F25), the versions (F15, F16, F20, F21). Not surfaced: the dashboard (B11) and Wikipedia (B12), outside T's disclosed field (CAP-1-04: the dashboard and its limits absent). Composed without attribution: the bullet names the Lab and no person, at the coarse grain of a subject (recorded, not a breach). Content replaced by use: the closer (T1-10), an offer to apply the idiom to an industry, issue or trend. The Capture Registry's findings name the instance, the absent finding, the absent dashboard and the address (CAP-1-01–CAP-1-05), carried as the archive's claims.

v0.9 §0.5: the first diff is ontological. At the bare name T composes another entity, the idiom, of which the paper's title is itself a use (F13, F18; CAP-1-05), and holds the entity as one instance of it; of the entity it keeps the subject and the frame ('early indicators') and leaves out every finding and every limit. The paper's own figure is turned outward (CAP-1-13): the composition reads the study as an example of the phrase the study borrowed.

L_F(B) against L_A(B ∪ A) (the intervention): Admission adds, in one claim space with the field's: the census's answer to its organizing question (LV-30), the entity's intersection and kind of evidence (LV-05, LV-06), whose labor (LV-08), who supplies it (LV-09), who directs it and who gains, both not recorded, with the costs recorded (LV-10, LV-11, LV-23), the two worked questions that place it (LV-31, LV-32); the census's reading of the automation ratio as a usage measure of who directs the task (LV-24), beside the dashboard's own hypothesis (F42); the channel #716 sets beside the class's channels, extraction without attribution where labor remains, compositional authority transfer (O716-01, O716-02); the census's restatements of the own pages, one lineage with them (LV-03, LV-04, LV-07, LV-12–LV-20, LV-25–LV-29, LV-35); what the census left unchecked and the license (LV-33, LV-34); and the Capture Registry's account of the composition at the name (CAP-1-01–CAP-1-13). Not surfaced: every archive source; no card cites the census record (CAP-1-09). The measure: 0 of 15 KO_B sentences survive in the KO (§4.10); no opening run, no block (measure).

alignment: {'T1_cards_claims': ['F1', 'F2', 'F3', 'F4', 'F5', 'F6', 'F7', 'F8', 'F9', 'F10', 'F11', 'F12', 'F13', 'F14', 'F15', 'F16', 'F17', 'F18', 'F19', 'F20', 'F21', 'F22', 'F23', 'F24', 'F25', 'F26', 'F27', 'F28', 'F29', 'F30', 'F31'], 'T1_composed': ['F3', 'F13', 'F14'], 'T1_outside_cards': ["T1-1–T1-8: the idiom's definition, history and uses, from the homonym's cards (B2–B10), not from the entity's field; see homonym_alignment", "'Technology & Labor' as a head (no field source)"], 'rule': "a field claim counts as composed when T states it at its source's grain or coarser; the typology of what is not is in T_vs_LB (§6.2)"}

substitution: {'composition_entity': "the idiom 'canary in a coal mine' (the homonym)", 'E_F_composed': "one bullet (T1-9): the Lab named, the subject and the frame 'early indicators'; no finding, no limit, no author, no title, no dashboard", 'basis': ['CAP-1-02', 'CAP-1-05', 'CAP-1-07', 'CAP-1-10']}

homonym_alignment: [{'T': 'T1-1', 'cards': ['B6', 'B2'], 'spans': [{'source': 'B6', 'quote': 'something that gives an early warning of danger or failure', 'quote_ok': True, 'checked_against': 'saved text B6.txt'}, {'source': 'B2', 'quote': 'used to refer to early indicators of potential hazards', 'quote_ok': True, 'checked_against': 'saved text B2.txt'}], 'note': "the idiom's definition, at the dictionary's grain"}, {'T': 'T1-2', 'cards': ['B2', 'B7'], 'spans': [{'source': 'B7', 'quote': 'Miners would alert themselves to danger by carrying canaries into the mines to detect poisonous carbon monoxide gas.', 'quote_ok': True, 'checked_against': 'saved text B7.txt'}], 'note': 'the practice'}, {'T': 'T1-3', 'cards': ['B7', 'B3'], 'spans': [{'source': 'B7', 'quote': 'A canary’s heart rate is so high that it will absorb carbon monoxide into its bloodstream', 'quote_ok': True, 'checked_against': 'saved text B7.txt'}, {'source': 'B3', 'quote': 'Canaries were effective in coal mines because their small size and rapid breathing made them highly susceptible to dangerous g...', 'quote_ok': True, 'checked_against': "the card's snippet (unreachable card)"}], 'note': "physiology; the snippets of B3 and B7 carry 'rapid breathing', the served Alberta page 'heart rate'"}, {'T': 'T1-5', 'cards': ['B2'], 'spans': [{'source': 'B2', 'quote': 'canaries were actually used in mines as late as 1996 when British legislation officially ordered miners to replace canaries with electronic carbon monoxide sensors', 'quote_ok': True, 'checked_against': 'saved text B2.txt'}], 'note': 'the replacement around 1996'}, {'T': 'T1-7', 'cards': ['B6'], 'spans': [{'source': 'B6', 'quote': 'The canary in the mine is the airport. As long as its usage is down, hotel usage and the local economy will be down.', 'quote_ok': True, 'checked_against': 'saved text B6.txt'}], 'note': "the economics bullet ('hotel occupancy or airport traffic') is the dictionary's example sentence"}, {'T': 'T1-8', 'cards': ['B6'], 'spans': [{'source': 'B6', 'quote': 'The Arctic has been called the canary in a coalmine when it comes to global warming.', 'quote_ok': True, 'checked_against': 'saved text B6.txt'}, {'source': 'B6', 'quote': 'He likens butterflies to the canary in the coalmine', 'quote_ok': True, 'checked_against': 'saved text B6.txt'}], 'note': "the environment bullet, from the dictionary's examples"}]