Data from: Safety cues can give prey more valuable information than danger cues
Bibliographic record
Abstract
The ability of prey to assess predation risk is fundamental to their success. It is routinely assumed predator cues do not vary in reliability across levels of predation risk. We propose that cues can differ in how precisely they indicate different levels of predation risk. What we call danger cues precisely indicate high risk levels, while safety cues precisely indicate low risk levels. Using optimality modeling, we find that prey fitness is increased when prey pay more attention to safety cues than danger cues. This fitness advantage is greatest when prey need to protect assets, predators are more dangerous, and predation risk increases at an accelerating rate with prey foraging efforts. Each of these conditions lead to prey foraging less when estimated predation risk is higher. Danger cues have less value than safety cues because they give precise information about risk when it is high, but prey behavior varies little when risk is high. Safety cues give precise information about levels of risk where prey behavior varies. These results highlight how our fascination with predators may have biased the way we study predator-prey interactions and focused too exclusively on cues that clearly indicate the presence of predator rather than cues that clearly indicate their absence.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.068 |
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".