The anti-predation benefit of flash displays is related to the distance at which the prey initiates its escape
Bibliographic record
Abstract
Flash behaviour is widespread in the animal kingdom and describes the exposure of a hidden conspicuous signal as an animal flees from predators. Recent studies have demonstrated that the signal can enhance survivorship by leading pursuing predators into assuming the flasher is also conspicuous at rest. Naturally, this illusion will work best if potential predators are ignorant of the flasher's resting appearance, which could be achieved if the prey flees while the predator is relatively far away. To test this hypothesis, we compared the survival of flashing and non-flashing computer-generated prey with different flight initiation distances (FIDs) using humans as model predators. This experiment found that flash displays confer a survivorship advantage only to those prey with a long FID. A complementary phylogenetic analysis of Australian bird species supports these results: after controlling for body size, species with putative flashing signals had longer FIDs than those without. Species with putative flashing signals also tended to be larger, as demonstrated in other taxa. The anti-predation benefit of flash displays is therefore related to the nature of escape behaviour. Since birds with hidden signals tend to flee at a distance, the flash display here is unlikely to function by startling would-be predators.
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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) | 0.003 | 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".