Probabilistic risk-assessment model for birds exposed to granular pesticides
Bibliographic record
Abstract
For granular formulations of pesticides, direct consumption by birds is generally the most important route of exposure. A probabilistic exposure model was developed that estimates how many pesticide granules a bird ingests and, from that, the quantity of pesticide ingested. This model, referred to as the "granular pesticide avian risk assessment model" (GranPARAM), has input variables not included in current screening-level assessments for granular pesticides, such as proportion of time for which birds forage in the field, grit ingestion rates, attractiveness of pesticide granules compared with natural grit, and proportions of soil particles and pesticide granules in the size range consumed by birds. For input variables that are uncertain, variable, or both, distributions are used rather than point estimates. Monte Carlo analysis is then performed to propagate input variable uncertainties through the exposure model for granular pesticides. The outputs from the exposure portion of GranPARAM are estimated pesticide doses for each of 20 birds of a selected species on each of 1000 fields. The dose for each bird is compared with a randomly chosen dose from the dose-response curve for that species or an appropriate surrogate. If the exposure dose for a bird exceeds the randomly chosen effects dose, the bird is considered dead; otherwise, the bird is assumed to be alive. Thus, the risk output from GranPARAM is a bar chart showing the percentages of fields with 0/20 dead birds, 1/20 dead birds, 2/20 dead birds, and so forth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".