Bibliographic record
Abstract
Aposematism, in which an organism's unprofitability to predators is advertised with a conspicuous signal, has been hypothesised to co-occur with an improved ability to survive being handled by a predator with negligible damage.In Chapter 1, the current literature on this "resistance to handling" and its association with aposematism is reviewed, including anecdotal accounts, experimental evidence and bio-physical considerations.Finally, future directions for this emerging research area are discussed.In Chapter 2, the relationship between resistance to handling and Batesian mimicry is tested through two experiments on field-caught specimens.The first evaluates the ability of insect species to survive a given amount of compressive force using the successive application of increasing weights, comparing Batesian mimics (Diptera: Syrphidae) to non-mimics (non-syrphid Diptera) and their models (Hymenoptera).The second experiment measures the force required to deform an insect by a given proportion.The relationship between kill weight titration and deformation metrics is also elucidated.Hymenopterans were the most resistant group, syrphid flies the least, and non-syrphid dipterans intermediate between the two.In all groups, both larger body size and a greater resistance to deformation were correlated with higher kill weights, while mimicry status and mimetic fidelity (once one controls for body size and phylogeny) were not.The implications and possible explanations for these findings are discussed.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".