Animal decisions and conservation: the recolonization of a severely polluted river by the Eurasian otter
Bibliographic record
Abstract
Abstract Animals make decisions relying on environmental cues associated to high survival or breeding success along their evolutionary history. However, because of rapid anthropogenic changes in the environment, they may lack useful cues, making bad decisions with potential consequences for individuals and populations. Contaminants are difficult or impossible to detect for animals, so polluted habitats could be used in spite of their dangerous effects. The Eurasian otterLutra lutrareoccupied the Guadiamar River (SW Spain) <1 year after a toxic spill that killed the fauna living in it. The levels of heavy metals and arsenic (As) in the river trophic web at that moment were probably harmful for otters. To investigate this, we determined the amount of several heavy metals including copper, cadmium, zinc (Zn) and lead (Pb) and metalloids such as As in otter faeces and estimated the exposure of otters to these elements as average ingestion. Concentrations of Zn, Pb and As were statistically higher in faeces collected along the Guadiamar River than in those collected along the Guadalete River (reference area). An ‘average otter’ in the Guadiamar River would consume 3–4 mg of Pb and more than 5 mg of As daily. Such doses must be hazardous for the species and challenge the usual assertion that otter presence is a good indicator of river quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".