Variable effects of snow conditions across boreal mesocarnivore species
Bibliographic record
Abstract
Mesocarnivores are increasingly recognized as key drivers of community dynamics, but the effects of bottom-up and abiotic factors on mesocarnivore populations remain poorly understood. We evaluated the effects of snow conditions, prey abundance, and habitat type on the distribution of five sympatric mesocarnivore species in interior Alaska using repeated snow-track surveys and occupancy modelling. Snow depth and snow compaction were the best predictors of mesocarnivore occupancy, with differential effects across species. Coyotes (Canis latrans Say, 1823) and red foxes (Vulpes vulpes (L., 1758)) occurred in areas of shallow, compact snow; Canada lynx (Lynx canadensis Kerr, 1792) occurred in areas of shallow, fluffy snow; and wolverines (Gulo gulo (L., 1758)) and marten (Martes americana (Turton, 1806)) occurred in areas of deep, fluffy snow. These findings indicate that altered snow conditions due to climate change may have strong direct effects on the distribution of northern mesocarnivores, with divergent effects across species.
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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) | 0.001 | 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".