Effects of atrazine on fish, amphibians, and reptiles: update of the analysis based on quantitative weight of evidence
Bibliographic record
Abstract
Quantitative weight of evidence (QWoE) provides a framework and process for evaluating different toxicological studies based on quality and relevance of the results. This framework allows for data from these studies to be combined in separate lines of evidence to address causality, and relevance to environmental risks. In 2014, such a QWoE that examined the body of available company reports and peer reviewed literature regarding the effects of the herbicide atrazine on fish, amphibians, and reptiles was published. Since that time, new studies have been conducted and/or published. One of the advantages of the QWoE framework is that additional information can be added as it becomes available. Thus, these new studies were evaluated in the same manner as previously and the new data incorporated into the existing QWoE. As before, the new updated QWoE was based on the same process of objective scoring of individual studies with respect to the quality of the methods and the relevance of individual responses to the apical endpoints of survival, growth, development, and reproduction. These new data did not identify new responses or indicate any relevant effects of atrazine. The new updated QWoE analysis concluded that atrazine does not adversely affect fish, amphibians, and reptiles, at environmentally relevant concentrations (<100 µg atrazine/L), which is consistent with the previous conclusions. These new studies and data are discussed in this paper and the accompanying supplement information provides detailed and transparent information to support these conclusions.
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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 | 0.287 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.042 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| 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".