Estimating the probability of bird mortality from pesticide sprays on the basis of the field study record
Bibliographic record
Abstract
The outcome of avian field studies was examined to model the likelihood of mortality. The data were divided into clusters reflecting the type of pesticide application and bird guilds present on site. Logistic regression was used to model the probability of a bird kill. Four independent variables were tested for their explanatory power: a variable reflecting acute oral toxicity and application rate; a variable reflecting the relative oral to dermal toxicity of the pesticides; Henry's law constant; and a variable reflecting possible avoidance of contaminated food items, the hazard factor (HF). All variables except for HF significantly improved model prediction. The relative dermal to oral toxicity, especially, was shown to have a major influence on field outcome and clearly must be incorporated into future avian risk assessments. The probability of avian mortality could be calculated from a number of current pesticide applications and the conclusion was made that avian mortality occurs regularly and frequently in agricultural fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".