Coral Reef Fish Rapidly Learn to Identify Multiple Unknown Predators upon Recruitment to the Reef
Bibliographic record
Abstract
Organisms often undergo shifts in habitats as their requirements change with ontogeny.Upon entering a new environment, it is vitally important to be able to rapidly assess predation risk. Predation pressure should selectively promote mechanisms that enable the rapid identification of novel predators. Here we tested the ability of a juvenile marine fish to simultaneously learn the identity of multiple previously unknown predators. Individuals were conditioned with a 'cocktail' of novel odours (from two predators and two non-predators) paired with either a conspecific alarm cue or a saltwater control and then tested for recognition of the four odours individually and two novel odours (one predator and one non-predator) the following day. Individuals conditioned with the 'cocktail' and alarm cue responded to the individual 'cocktail' odours with an antipredator response compared to controls. These results demonstrate that individuals acquire recognition of novel odours and that the responses were not due to innate recognition of predators or due to a generalised response to novel odours. Upon entering an unfamiliar environment prey species are able to rapidly assess the risk of predation, enhancing their chances of survival, through the assessment of chemical stimuli.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".