Effects of Atrazine in Fish, Amphibians, and Reptiles: An Analysis Based on Quantitative Weight of Evidence
Bibliographic record
Abstract
A quantitative weight of evidence (WoE) approach was developed to evaluate studies used for regulatory purposes, as well as those in the open literature, that report the effects of the herbicide atrazine on fish, amphibians, and reptiles. The methodology for WoE analysis incorporated a detailed assessment of the relevance of the responses observed to apical endpoints directly related to survival, growth, development, and reproduction, as well as the strength and appropriateness of the experimental methods employed. Numerical scores were assigned for strength and relevance. The means of the scores for relevance and strength were then used to summarize and weigh the evidence for atrazine contributing to ecologically significant responses in the organisms of interest. The summary was presented graphically in a two-dimensional graph which showed the distributions of all the reports for a response. Over 1290 individual responses from studies in 31 species of fish, 32 amphibians, and 8 reptiles were evaluated. Overall, the WoE showed that atrazine might affect biomarker-type responses, such as expression of genes and/or associated proteins, concentrations of hormones, and biochemical processes (e.g. induction of detoxification responses), at concentrations sometimes found in the environment. However, these effects were not translated to adverse outcomes in terms of apical endpoints. The WoE approach provided a quantitative, transparent, reproducible, and robust framework that can be used to assist the decision-making process when assessing environmental chemicals. In addition, the process allowed easy identification of uncertainty and inconsistency in observations, and thus clearly identified areas where future investigations can be best directed.
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 | 0.339 | 0.536 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.075 | 0.032 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".