The key mimetic features of hoverflies through avian eyes
Bibliographic record
Abstract
Batesian mimicry occurs when a palatable species (the mimic) gains protection from predators by resembling an unpalatable or otherwise protected species (the model). While some mimetic species resemble their models closely, other species ('imperfect mimics') are thought to bear only a crude likeness. In an earlier study, pigeons (Columba livia) were trained to recognize wasp images in one experiment and non-mimetic (NM) fly images in another by rewarding the pigeons for pecking on the respective image types. These pigeons were subsequently presented with different images, including seemingly wasp-like hoverfly species, and the recorded peck rates on these images were used as a measure of the pigeons' perception of the hoverflies' mimetic similarity. To identify a candidate set of morphological features that the pigeons used when assessing this mimetic similarity, we first extracted a range of biometrical measurements from images originally presented to the pigeons. We then repeatedly optimized an empirical model in an attempt to match the recorded pigeon peck rates while using as few biometrical features as input as possible. Our models were able to fit the pigeon peck rates with considerable accuracy even while excluding many input features. Antennal length, a feature commonly used to discriminate between flies and wasps, was regularly retained as an input variable, but overall a different set of biometrical features was important for predicting the peck rates of pigeons rewarded for identifying wasps compared to those rewarded for identifying NM flies. In highlighting the importance of specific biometrical features in promoting mimicry and the irrelevance of others, our optimized models provide an explanation as to why certain species that appear to be poor mimics to humans are judged to be good mimics by birds.
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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) | 0.001 | 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".