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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.041 |
| 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 | 0.002 |
| Research integrity | 0.036 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".