Assessment of Pesticide Exposures for Epidemiologic Research: Measurement Error and Bias
Bibliographic record
Abstract
Although numerous epidemiologic studies have been conducted to evaluate acute and chronic health effects associated with pesticide exposures, results of these studies are not consistent, may often be biased, and are generally not supported with accurate pesticide exposure data. Inadequate measurement of pesticide exposure, or preferably dose, is a major factor limiting the value of study results. Since it is generally not possible to measure pesticide exposures retrospectively, and not costeffective or practical to measure exposures prospectively, alternative techniques must be developed and evaluated for use in epidemiologic research. Past exposure assessment methods, their associated biases, and current efforts are described.
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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 | 0.205 | 0.335 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".