Learned Recognition of Heterospecific Alarm Signals: The Importance of a Mixed Predator Diet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it