Diel movement patterns influence daily variation in wolf kill rates on moose
Bibliographic record
Abstract
Abstract Variation in predation can have important consequences for predators and prey, but little is known about associated mechanisms. Diel interactions between predators and prey are commonly assumed to be influenced by movement speeds of both predators and prey individuals,sensuthe ideal gas model, but the influencing factors of diel predation dynamics have yet to be empirically examined. In this study, we apply principles of the ideal gas model to predict diel variation in kill frequency of moose (Alces alces) by wolves (Canis lupus) in northern Ontario, Canada based onGPSradio‐telemetry data combined with field verification of kills. We usedGPStelemetry data from wolves and moose combined with a unique data set on the diel pattern of wolf kills to test whether predator movement rate, prey movement rate, and ambient light condition influence diel variation in kill rates of wolves on moose. Our results indicate that the kill rate between wolves and moose was principally related to the effective movement rate of predators and prey, as predicted by the ideal gas model. We found little evidence that light conditions had any effect on kill rates, but rather the majority of kill rate variation corresponded to wolf movement rate, which was over an order of magnitude higher than that of moose.
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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) | 0.001 | 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".