Predator Inspection Behaviour in a Characin Fish: an Interaction between Chemical and Visual Information?
Bibliographic record
Abstract
Abstract Recent evidence suggests that predator inspection behaviour by Ostariophysan prey fishes is regulated by both the chemical and visual cues of potential predators. In laboratory trials, we assessed the relative importance of chemical and visual information during inspection visits by varying both ambient light (visual cues) and predator odour (chemical cues) in a 2 × 2 experimental design. Shoals of glowlight tetras (Hemigrammus erythrozonus) were exposed to a live convict cichlid (Archocentrus nigrofasciatus) predator under low (3 lux) or high (50 lux) light levels and in the presence of the odour of a cichild fed tetras (with an alarm cue) or swordtails (Xiphophorus helleri, with an alarm cue not recognized by tetras). Tetras exhibited threat‐sensitive inspection behaviour (increased latency to inspect, reduced frequency of inspection, smaller inspecting group sizes and increased minimum approach distance) towards a predator paired with a tetra‐fed diet cue, regardless of light levels. Similar threat‐sensitive inspection patterns were observed towards cichlids paired with a swordtail‐fed diet cue only under high light conditions. Our data suggest that chemical cues in the form of prey alarm cues in the diet of the predator, are the primary source of information regarding local predation risk during inspection behaviour, and that visual cues are used when chemical information is unavailable or ambiguous.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".