Data from: Olfactory cues of habitats facilitate learning about landscapes of fear
Bibliographic record
Abstract
Across landscapes, prey are exposed to different levels of predation risk within different habitats. However, little is known about how prey learn about risk in different habitat types. Here, we examined if wood frog tadpoles, Lithobates sylvatica, use olfactory cues from two distinct, plant-dominated habitats (cattail and pond weed) to learn about the overall risk within a habitat and the risk posed by a specific predator species within different habitats. In our first experiment, tadpoles experienced both a high-risk and a low-risk habitat before being tested for habitat-specific neophobic responses, a cognitive trait expressed in high-risk but not low-risk environments. In the second experiment, we taught tadpoles to recognise a predator in one habitat while the other one was never associated with a predator. Tadpoles were then tested for their responses to the predator and a control in both habitats. Our results showed that high-risk cattail tadpoles developed habitat-specific neophobia. However, high-risk pond weed tadpoles developed a generalised neophobia, responding to the novel cues irrespective of the habitat where they were tested. We also found that the habitat in which prey learned the identity of a specific predator did not affect their responses to that predator when tested in different habitats. Our results provide support for the use of olfactory habitat cues by prey to learn about predation risk across landscapes, suggesting unrecognised nuances to how prey use such cues to learn about predation risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.274 | 0.067 |
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".