Bibliographic record
Abstract
Prey individuals employ several adaptive behaviours to reduce predation risk. We need to learn how those behaviours interact in an overall strategy of risk management, how strategies vary with changing conditions, and whether some behaviours might compensate for others. I addressed these issues with manipulative experiments evaluating how snowshoe hares’ ( Lepus americanus) vigilance varies with their giving-up densities (GUDs) in artificial food patches. I tested whether the results, collected when there was no evidence of predation, were congruent with an earlier study under higher predation. When predator sign was common, vigilance depended directly on habitat. But when risk was low, habitat’s influence on vigilance was indirect. Hares were least vigilant during the new moon where the distance to escape habitat was far, but only in open risky habitat. Hares were more vigilant during the full moon, but only at stations far from escape habitat. Moon phase and additional cover had no effect on GUDs that were highest at open risky stations far from escape habitat. The results suggest that reduced risk allowed hares to allocate less time to vigilance, but they needed to forage for similar amounts of food during each moon phase to maintain their energetic state.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".