The size of hoverfly mimics relative to their models and the potential role of size in the evolution of mimicry
Bibliographic record
Abstract
Body size has received little attention as a mimetic trait, despite evidence that predators can discriminate mimics and models based on size.This thesis aims to explore the potential role of size in the evolution of mimicry.More specifically, it will investigate if mimics that are readily discriminable in size from their models experience reduced selection on colour pattern similarity.Firstly, we investigated if selection on colour similarity would decrease when size was highly discriminable for computer-generated prey and results indicated that when mimics and models were highly discriminable in size, there was less selection on colour.We then also investigated if there was a relationship between mimetic fidelity and the size of mimics relative to their model in hoverflies (Diptera: Syrphidae).No significant relationship between mimetic fidelity and relative size was found; instead a positive relationship between mimetic fidelity and the size of their hymenopteran models was found.Overall, this thesis aims to explore the role size may have in the evolution of mimicry.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".