Analysis of a database of pesticide residues on plants for wildlife risk assessment
Bibliographic record
Abstract
Current methods to estimate exposure of wildlife to pesticides from diet depend on a database of published residue concentrations on crop plants normalized to a standard application rate to obtain a residue from a unit dosage (RUD). This database, first published in the early 1970s, was updated in the 1990s. For each category of crops, maximum and mean residues are determined and used to extrapolate concentrations on plants across application rates in calculations of risk. The present study aims to update the database, to examine the validity of extrapolating RUD values across application rates, and to improve the categorization of crops using crop morphology and cultivation methods. The slope of the linear regression of residue concentrations against application rate in 41 trials was significantly different from one in all but five cases. This supports the assumption that residue concentrations are directly proportional to the application rate, although less than half the variance in residue concentrations was explained by the linear model. Residues on leaves were partitioned into eight categories of crops using information regarding plant morphology and cultivation method. Fruit size was an additional variable useful for segregating residues into four categories: Small fruits, large fruits, pods, and grains. The proposed changes increase the amount of variance explained in the residue database from 19 to 32%. Depending on the crop category, residues on fruits were 2- to 16-fold lower than those on leaves. Residue concentrations on leaves of short plants were more than fourfold higher than those on leaves of tall plants. Descriptive statistics are provided for each of the proposed crop categories.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".