White-tailed Deer (<i>Odocoileus virginianus</i>) Subsidize Gray Wolves (<i>Canis lupus</i>) During a Moose (<i>Alces americanus</i>) Decline: A Case of Apparent Competition?
Bibliographic record
Abstract
Moose (Alces americanus) in northeastern Minnesota have declined by 55% since 2006. Although the cause is unresolved, some studies have suggested that Gray Wolves (Canis lupus) contributed to the decline. After the Moose decline, wolves could either decline or switch prey. To determine which occurred in our study area, we compared winter wolf counts and summer diet before and after the Moose decline. While wolf numbers in our study area nearly doubled from 23 in winter 2002 to an average of 41 during winters 2011–2013, calf:cow ratios (the number of calves per cow observed during winter surveys) in the wider Moose range more than halved from 0.93 in 2002 to an average of 0.31 during 2011–2013. Compared to summer 2002, wolves in summers 2011–2013 consumed fewer Moose and more White-tailed Deer (Odocoileus virginianus). While deer densities were similar during each period, average vulnerability, as reflected by winter severity, was greater during 2011–2013 than 2002, probably explaining the wolf increase. During the wolf increase Moose calves remained a summer food item. These findings suggest that in part of the Moose range, deer subsidized wolf numbers while wolves also preyed on Moose calves. This contributed to a Moose decline and is a possible case of apparent competition and inverse-density-dependent predation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".