Sophisticated early life lessons: threat-sensitive generalization of predator recognition by embryonic amphibians
Bibliographic record
Abstract
The ability to develop effective antipredator responses early in life should be strongly promoted by natural selection. Recent work has shown that embryonic amphibians can learn to recognize predators even before they hatch. Here, we showed that embryonic woodfrogs, Rana sylvatica, learned the danger level associated with a predator prior to hatching and generalized their learned recognition to other similar predators with which the woodfrogs lacked experience. Embryos exposed to salamander odor (SO) paired with injured tadpole cues learned to recognize the salamander Ambystoma tigrinum, but those exposed to SO paired with well water did not. When we increased the concentration of alarm cues to which embryos were exposed, tadpoles showed stronger response to salamander cues. In addition, the tadpoles generalize their learned response to the odor of closely related newts Cynops pyrrhogaster but not Xenopus frogs. In accordance with the Predator Recognition Continuum Hypothesis, the ability to generalize was dependent on the threat level of the predator. Our results highlight the sophistication of learned responses to predators by embryonic amphibians and stress the need for studies in other taxa.
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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".