Emerging investigator series: use of behavioural endpoints in the regulation of chemicals
Bibliographic record
Abstract
Interest in behavioural ecotoxicology is growing, partly due to technological and computational advances in recording behaviours but also because of improvements of detection capacity facilitating reporting effects at environmentally relevant concentrations. The peer-reviewed literature now contains studies investigating the effects of chemicals, including pesticides and pharmaceuticals, on migration, dispersal, aggression, sociability, reproduction, feeding and anti-predator behaviours in vertebrates and invertebrates. To understand how behavioural studies could be used in regulatory decision-making we: (1) assessed the legal obstacles to using behavioural endpoints in EU chemicals regulation; (2) analysed the known cases of use of behavioural endpoints in EU chemicals regulation; and (3) provided examples of behavioural endpoints of relevance for population level effects. We conclude that the only legal obstacle to the use of behavioural endpoints in EU chemicals regulation is whether an endpoint is considered to be relevant at the population level or not. We also conclude that ecotoxicity studies investigating behavioural endpoints are occasionally used in the EU chemicals regulation, and underscore that behavioural endpoints can be relevant at the population level. To improve the current use of behavioural studies in regulatory decision-making contribution from all relevant stakeholders is required. We have the following recommendations: (1) researchers should conduct robust, well-designed and transparent studies that emphasize the relevance of the study for regulation of chemicals; (2) editors and scientific journals should promote detailed, reliable and clearly reported studies; (3) regulatory agencies and the chemical industry need to embrace new behavioural endpoints of relevance at the population level.
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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 | 0.061 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.036 |
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".