Phase-, season-, cycle number or coat colour state-specific estimates of capture probabilities and transition probabilities for snowshoe hares in Kluane, Yukon, Canada, 1977 – 2020. Average winter snow depth data in Kluane, Yukon, Canada are also presented for the period 1988 – 2021 from Does coat colour influence survival? A test in a cyclic population of snowshoe hares
Bibliographic record
Abstract
Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares (Lepus americanus) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".