Does defensive posture increase mimetic fidelity of caterpillars with eyespots to their putative snake models?
Bibliographic record
Abstract
Abstract Organisms often evolve behaviours that increase or reinforce the protection from predators afforded by their morphological defences. For example, mimetic animals may adopt postures or locomotory behaviours that emulate a characteristic feature of their model to increase predator deception. Caterpillars with eyespots are thought to mimic snakes, and when threatened many of these caterpillars adopt a posture that appears to enhance this resemblance. Herein we evaluate the quantitative strength of evidence of behavioural mimicry in the caterpillars of 14 species by comparing how closely a series of putative snake-mimicking caterpillars resemble snakes while at rest and when threatened. Specifically, we quantified the head morphology and eye position of a range of snake species, as well as the shape of the apparent head (i.e. anterior body segments) and position of eyespots in caterpillars resting or in their defensive posture. This allowed us to objectively examine evidence for an increased resemblance to either snakes generally, or to Viperidae snakes specifically, upon adopting the defensive posture. Widening the anterior body segments during the defensive posture typically made caterpillars appear more viper-like as opposed to more snake-like in general. Enhanced resemblance to vipers upon mounting the defensive posture was apparent only from the dorsal view. Laterally, caterpillars more closely resembled snakes in the resting posture and shifting to the defensive posture instead reduced mimetic fidelity. Overall we found evidence for behavioural mimicry in all 14 species examined. We highlight that objectively quantifying mimetic fidelity can help identify key features involved in deception.
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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 | 0.001 |
| 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".