Comment: Regulation of moose populations by wolf predation
Bibliographic record
Abstract
We discuss regulation of moose (Alces alces) populations by wolves (Canis lupus) in the context of a recent article by Eberhardt (L.L. Eberhardt. 1997. Can. J. Zool. 75: 1940-1944), who contended that the killing rate of moose by wolves was constant. Further, he argued that wolf population size was proportional to prey density, and that wolf predation exerted a regulatory effect on ungulate-prey numbers. We argue that this combination of functional and numerical responses results in density-independent predation that cannot regulate prey numbers. We discuss the present understanding of wolf-moose interactions and conclude that there is evidence suggesting density dependence in both functional and numerical responses. Further, we conclude that predation by wolves is density-dependent, at least at low moose densities, and therefore can act as a regulatory factor.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.041 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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".