The Evolution of Warning Signals as Reliable Indicators of Prey Defense
Why this work is in the frame
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Bibliographic record
Abstract
It is widely argued that defended prey have tended to evolve conspicuous traits because predators more readily learn to avoid defended prey when they are conspicuous. However, a rival theory proposes that defended prey have evolved such characters because it allows them to be distinguished from undefended prey. Here we investigated how the attributes of defended (unprofitable) and undefended (profitable) computer-generated prey species tended to evolve when they were subject to selection by foraging humans. When cryptic forms of defended and undefended species were similar in appearance but their conspicuous forms were not, defended prey became conspicuous while undefended prey remained cryptic. Indeed, in all of our experiments, defended prey invariably evolved any trait that enabled them to be distinguished from undefended prey, even if such traits were cryptic. When conspicuous mutants of defended prey were extremely rare, they frequently overcame their initial disadvantage by chance. When Batesian mimicry of defended species was possible, defended prey evolved unique traits or characteristics that would make undefended prey vulnerable. Overall, our work supports the contention that warning signals are selected for their reliability as indicators of defense rather than to capitalize on any inherent educational biases of predators.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it