Temporal clustering of prey in wildlife passages provides no evidence of a prey-trap
Bibliographic record
Abstract
Wildlife passages are structures built across roads to facilitate wildlife movement and prevent wildlife collisions with vehicles. The efficacy of these structures could be reduced if they funnel prey into confined spaces at predictable locations that are exploited by predators. We tested the so-called prey-trap hypothesis using remote cameras in 17 wildlife passages in Quebec, Canada from 2012 to 2015 by measuring the temporal occurrence of nine small and medium-sized mammal taxa (< 30 kg) that we classified as predators and prey. We predicted that the occurrence of a prey-trap would be evidenced by greater frequencies and shorter latencies of sequences in which predators followed prey, relative to prey-prey sequences. Our results did not support the prey-trap hypothesis; observed prey-predator sequences showed no difference or were less frequent than expected, even when prey were unusually abundant or rare or at sites with higher proportions of predators. Prey-predator latencies were also 1.7 times longer than prey-prey sequences. These results reveal temporal clustering of prey that may dilute predation risk inside wildlife passages. We encourage continued use of wildlife passages as mitigation tools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".