Latent inhibition of predator recognition by embryonic amphibians
Bibliographic record
Abstract
To avoid being captured, prey animals need to be able to distinguish predators from non-predators. Recent studies have shown that amphibians can learn to recognize their future predators while in the egg. Here, we investigated whether amphibians would similarly be able to learn to recognize non-predators while in the egg. We exposed newly laid wood frog eggs to the odour of tiger salamander or a water control daily for 5 days. After hatching, the wood frog larvae were raised for two weeks at which time we tried to condition them to recognize the salamander as a predator. Larvae were exposed to injured conspecific cues paired with salamander odour, a well-established mode of learning for aquatic prey. When subsequently tested for their response to salamander odour, the larvae pre-exposed to water as embryos showed significant anti-predator responses. However, larvae pre-exposed to the salamander odour as embryos showed no learning of the predator, indicating that they had already learned to recognize the salamander as a non-predator. These results indicate that amphibian embryos can (i) learn to recognize stimuli as non-threatening and (ii) remember it for at least two weeks. The widespread ability of prey to learn to recognize non-predators might explain the persistence of injured conspecific cues as a reliable mechanism for learned predator recognition.
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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) | 0.002 | 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".