Diet reconstruction of wolves following a decline in Manitoba moose populations
Bibliographic record
Abstract
Moose populations in southern Manitoba have declined. Although the primary cause of this decline is unclear, wolf predation is considered a main contributor because wolves have limited moose populations elsewhere. To help understand the role of wolf predation in limiting moose populations we reconstructed the proportional diet of wolves using stable isotope analysis and a Bayesian stable isotope mixing model (MixSIAR). We tested the assumptions of MixSIAR by running sensitivity analyses on trophic discrimination and prior information. We observed differences in wolf diet in areas where moose and deer coexist and are spatially separated, with changes both seasonally and annually. Our results suggest white-tailed deer were preyed on during winter, even in areas where deer are locally rare, suggesting prey catchability is more important than local prey density. Seasonal changes in prey accessibility strongly affect wolf predation rates, and manipulating alternative prey populations could mitigate predation impacts on moose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".