Learned Recognition of Heterospecific Alarm Signals: The Importance of a Mixed Predator Diet
Bibliographic record
Abstract
A wide diversity of aquatic organisms release alarm signals upon being attacked by a predator. Alarm signals may ‘warn’ nearby individuals of danger. Moreover, the signals may be important in facilitating learned recognition of unknown stimuli. It is common for different prey species to respond to each other’s chemical alarm signals. In many cases, the responses are learned but no learning mechanisms have been identified to date. In this study we tested whether prey fish can learn the identity of an unknown alarm signal when they detect it in association with conspecific alarm cues in the diet of a predator. Chemical alarm cues are known to be conserved in the diet of predators. We conditioned fathead minnows (Pimephales promelas) with chemical stimuli from predatory yellow perch (Perca flavescens) fed a mixed diet of minnows and brook stickleback (Culaea inconstans), perch fed a mixed diet of swordtails (Xiphophorus helleri) and stickleback or distilled water. Minnows were subsequently exposed to chemical alarm cues of injured stickleback alone. Those minnows previously conditioned with perch fed a mixed diet of minnows and stickleback increased their use of shelter and ‘froze’ significantly more than minnows previously conditioned with perch fed a diet of swordtails and stickleback or those exposed to distilled water. These data demonstrate a mechanism by which minnows can learn the identity of a heterospecific alarm signal.
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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 | 0.000 |
| 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".