Flash behavior increases prey survival
Bibliographic record
Abstract
Flash behavior, in which otherwise cryptic prey exhibit conspicuous coloration or noise when fleeing from potential predators, has been postulated to hinder location of prey once they become stationary. Here, using artificial computer-generated prey and humans as visual predators, we show that human subjects are more likely to abandon their search for prey that flash, compared to continuously cryptic fleeing controls. Survivorship of flashing prey was an additional 20% higher than the survivorship of continuously cryptic prey, depending on the background against which it was depicted. This survivorship advantage was consistent regardless of whether prey showed flash colors continuously or intermittently during flight. The advantage over continuously cryptic prey was highest when the flashing prey was presented first. Likewise, the more search areas containing no prey that the volunteers had initially viewed, the more likely they were to give up when there was a cryptic prey present. Collectively, these 3 findings indicate that volunteers inferred the flashing prey was absent from the search area when they failed to see a prey in the same form as they saw it move. Our results demonstrate first proof of concept: flash behavior, widely seen in taxa from insects to mammals, is an effective antipredator escape mechanism.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".