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Record W6950212674 · doi:10.5683/sp3/zmic14

Occupational Surveillance in Canada, 1965-1991: Causes Specific Mortality Among Workers

2000· dataset· en· W6950212674 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2000
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceSample (material)EpidemiologyMortality rateOccupational safety and healthPopulationOccupational medicineMEDLINE

Abstract

fetched live from OpenAlex

A previous publication for the province of British Columbia (Occupational mortality in British Columbia 1950-1978) was published in 1986 as Statistics Canada catalogueno. 84-544 (ISBN 0-660-59382-32872-X).This new publication helps identify occupational groups across Canada with excessive mortality due to specific causes. It also provides a Canadian monitoring system to detect previously unsuspected associations between, for example, cancer and occupationand provide a powerful tool for both generating and testing hypotheses. The publication presents the results of a longitudinal follow-up of the 10% Canadian Occupational Cohort, a sample of 700,000 individuals, both women and men, in the Canadian workforce during the period 1965-71, linked to the Canadian Mortality DataBase (CMDB) for 1965-1991.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.036
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.027
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.272
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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