Constraints on active-consumption rates in gray wolves, coyotes, and grizzly bears
Bibliographic record
Abstract
Predators' feeding strategies lie on a continuum between energy maximizers, who maximize the energy obtained from a patch of food, and time minimizers, who minimize the time required to get a fixed ration of food from a patch. Carnivores that feed on large prey should adopt a time-minimizing strategy by maximizing their active-consumption rate (ACR) if they evolved under conditions of high competition from group members, and conversely adopt an energy-maximizing strategy if they evolved under conditions of low competition from group members and were thus able to monopolize their prey. By provisioning animals with large pieces of ungulate carcasses, we measured ACR for captive gray wolves (Canis lupus), coyotes (Canis latrans), and grizzly bears (Ursus arctos). In accordance with a conspecific-competition hypothesis, ACR increased with sociality. Other factors influencing ACR included subject body mass and food type, ACR being significantly faster on muscle and organs than on bone and hide. Measuring ACR is crucial to empirical and theoretical studies assessing foraging decisions and may be used as an indicator of an animal's competitive environment.
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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 | 0.000 |
| 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".