Additional file 1: Table S1. of Lung cancer risk among workers in the construction industry: results from two case–control studies in Montreal
Bibliographic record
Abstract
Odds ratios of lung cancer and ever being employed in the construction industry, the reference unexposed category being either all workers outside the construction industry or all blue collar workers outside the construction industry, in Study I and Study II, separately. Table S2. Odds ratios of lung cancer and ever being employed in the construction industry, the reference unexposed category being either all workers outside the construction industry or all blue collar workers outside the construction industry, pooled set of two studies conducted in Montreal, Canada, restricted to self-respondents. Table S3. Odds ratios of lung cancer and ever being employed in the construction industry, the reference unexposed category being all blue collar workers outside the construction industry, pooled set of two studies, with three different criteria for defining a blue collar worker*. Table S4. Odds ratios of lung cancer and exposure to selected chemical agents in analyses restricted to construction workers, pooled set of studies, using two sets of covariates. Table S5. Odds ratios of lung cancer and exposure to selected chemical agents in analyses restricted to construction workers, pooled set of studies, using two sets of covariates, and restricted to self-respondents. Table S6. Odds ratios of lung cancer and ever being employed in the construction industry, the reference unexposed category being either all workers outside the construction industry or all blue collar workers outside the construction industry, with or without adjustment for socio-economic status (SES, represented by median family income for census tract and education level). Table S7. Odds ratios of lung cancer and exposure to selected chemical agents in analyses restricted to construction workers, pooled set of studies, with or without adjustment for socio-economic status (SES, represented by median family income for census tract and education level). (DOCX 81 kb)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.722 | 0.035 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".