Nonagricultural and residential exposures to pesticides.
Bibliographic record
Abstract
Epidemiologic studies and risk assessments conducted to assess the chronic effects of pesticides are limited by inadequate measurements of pesticide exposures, and surrogates for these data are frequently used. In this paper, pesticide use and absorbed dose previously measured in residential and occupational settings are used to evaluate the hypothesis that there is a relationship between pesticide use and exposure. For homeowner applicators of 2,4-dichlorophenoxyacetic acid (2,4-D) and chlorpyrifos, exposures were poorly correlated with the amount of herbicide used (r2 = 0.01 to 0.40); however, exposures from a granular product were consistently less than those with liquid formulation. For professional landscape applicators, exposure over 14 days and 7 days of use was poorly correlated with the amount of 2,4-D sprayed (r2 = 0.17 and 0.21, respectively). However, inclusion of the type of spray nozzle used and the use of gloves while spraying in the model explained increased predictability and explained 68% of the variation.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".