Sensory complement model helps to predict diel alarm response patterns in juvenile Atlantic salmon (Salmo salar) under natural conditions
Bibliographic record
Abstract
Fish rely on both chemical and visual cues to evaluate predation risk. Decisions with respect to activity partitioning in time (i.e., night vs. day) rely on accurate assessment of predation risk relative to energy intake; predation risk is generally thought to be lower at night at the expense of feeding opportunities. At night, the sensory complement model predicts greater reliance on chemical perception of risk. Under this condition, a lower ability to use vision should result in a more conservative response to chemical cues than during the day. We tested this hypothesis under natural conditions by comparing the alarm response of young-of-the-year Atlantic salmon ( Salmo salar L., 1758) under summer day and night conditions in salmon nursery streams. We found that salmon responded to the alarm cues to a significantly greater extent at night. This suggests that the sensory complement model may be correct and that nocturnal perception of risk may be generally higher than previously believed for juvenile salmon in the wild. In the absence of a more precise indicator of risk (e.g., vision), a greater reliance on chemosensory risk assessment at night may cause fish to shift to more risk-adverse behaviour.
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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 | 0.000 | 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".