Pesticide Use and Asthma in Alberta Grain Farmers
Bibliographic record
Abstract
A study of the respiratory health of grain farmers in Alberta, Canada was carried out in March 2002. Two populations were identified: members, in 1983, of a province-wide farm organisation, and grain farmers registered with the provincial agriculture department. A telephone interview addressed pesticide use (using pre-circulated trade names), chronic disease and respiratory symptoms. Pesticide ingredients were identified from provincial crop protection guides. Total years of use were calculated for seven chemical groups. Consent for linkage to administrative health records was obtained in 2009. A likelihood score (Lscore) is computed, relating symptoms to asthma diagnosis. Self-reported asthma and the Lscore are examined against duration of pesticide exposures. Of the 10,767 farmers listed, 2426 were still living, had farmed grain and were interviewed; 1371 were re-contacted and matched to health records. After allowance for confounders, years of exposure to phenoxy compounds are related to self-reported asthma and Lscore. Compared to no exposure, the adjusted odds ratios (95% Confidence Intervals for self-reported asthma for short, medium and long exposure to phenoxy compounds are 1.29 (0.66-2.52), 2.52 (1.25-5.09), and 3.18 (1.54-6.58), and for Lscore are 1.19 (0.91-1.55), 1.50 (1.13-1.99), and 1.58 (1.18-2.12). We conclude that lifetime exposure to phenoxy herbicides is associated with an increased risk of asthma.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".