Behavioural ecotoxicology, an “early warning” signal to assess environmental quality
Bibliographic record
Abstract
BACKGROUND: In this review, the position of behavioural ecotoxicology within the available means to assess the status of marine environments is described as filling the gap for the needed "early warning" signals. A few examples of studies performed since the 1960s are discussed to highlight the sensitivity of these approaches in investigating the effects of chemicals, including priority pollutants and emerging contaminants, relative to conventional toxicity tests measuring survival. DISCUSSION: The advantage of the behavioural response is due to the integration of biochemical and physiological processes that reflect changes at higher levels of organisation with ecological relevance. Avoidance often represents a behavioural symptom easily detected in many animals exposed to contaminants and would be a useful test to explore more widely. This rapid response would reflect a defence mechanism protective against further exposure and the potential development of more pronounced deleterious effects, whilst in some cases, escape could lead to the relocation of a species with negative consequences. An investigation of the avoidance behaviour of mud shrimp, Corophium volutator, along with the chemical analyses of sediments and amphipods to assess the quality of harbour sediments is summarised. The body burden of the amphipods was 1,000 times lower than the one associated with narcosis, emphasizing the sensitivity of this endpoint. The application of this acute toxicity test is briefly compared to additional work that involved intertidal mussels collected in the field. CONCLUSIONS: Recent research undertaken with mud snails, Ilyanassa obsoleta, and harbour sediments confirmed the usefulness of the escape behaviour as an assessment tool. However, the limits of the state of knowledge regarding the fate of contaminants in species with the ability to metabolise contaminants is further discussed along with directions to be pursued to address questions arising from the reviewed literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".