Data from: Better the devil you know? how familiarity and kinship affect prey responses to disturbance cues
Bibliographic record
Abstract
Prey can greatly improve their odds of surviving predator encounters by eavesdropping on conspecific risk cues, but the reliability of these cues depends on both previous accuracy as well as the cue’s relevance. During a predator chase, aquatic prey release chemical disturbance cues that may vary in their reliability depending on the individuals receiving them. Thus, prey may rely differentially on disturbance cues from familiar individuals (due to previous experience) or from kin (due to their relatedness). We examined the responses of wood frog (Lithobates sylvaticus) tadpoles to disturbance cues from familiar vs. unfamiliar conspecifics and kin vs. non-kin. In accordance with our prediction, tadpoles responded differently to disturbance cues from familiar vs. unfamiliar conspecifics. Tadpoles receiving disturbance cues from unfamiliar individuals displayed a fright response, whereas tadpoles ignored disturbance cues from familiar individuals. Tadpoles may have habituated to familiar cues since they were unaccompanied by a true threat, hence rendering these cues functionally unreliable. Tadpoles responded similarly to disturbance cues from related and unrelated individuals suggesting they were similarly reliable and this mirrors the matching reliability of prey responses to damage-released alarm cues from kin and non-kin. Our findings shed light on a seldom-studied chemical communication system in aquatic prey.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.024 |
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".