Chemosensory predator recognition induces defensive behavior in the slow-worm (<i>Anguis fragilis</i>)
Bibliographic record
Abstract
Many antipredator adaptations are induced or mediated by the ability of the prey to recognize chemical cues from the predator. This ability is particularly advantageous for organisms whose environment precludes the effective use of other sensory systems, such as fossorial lizards. We tested the ability of the slow-worm, Anguis fragilis Linnaeus, 1758, a semifossorial legless lizard, to detect and discriminate chemical stimuli arising from potential predators. We compared rates of tongue-flicks to swabs impregnated with scents from the smooth snake (Coronella austriaca Laurenti, 1768), a sympatric predator, the grass snake (Natrix natrix (Linnaeus, 1758)), a sympatric but non-saurophagous predator, and the three-toed skink (Chalcides striatus (Cuvier, 1829)), a sympatric insectivorous, and thus innocuous, skink. Differential tongue-flick rates suggest that scents of smooth snakes were recognized by slow-worms. Moreover, scents of smooth snakes were quickly avoided and elicited defensive behaviors much more often than any other scent, suggesting that chemical cues are a reliable means of assessing the snake's presence. These chemosensory capacities would be evolutionarily advantageous to avoid predation by snakes and are likely to represent a component of the suite of adaptations associated with low-visibility habitats.
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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".