Snow avalanches are a primary climate-linked driver of mountain ungulate populations
Bibliographic record
Abstract
Snow is a major, climate-sensitive feature of the Earth's surface and catalyst of fundamentally important ecosystem processes. Understanding how snow influences sentinel species in rapidly changing mountain ecosystems is particularly critical. Whereas effects of snow on food availability, energy expenditure, and predation are well documented, we report how avalanches exert major impacts on an ecologically significant mountain ungulate - the coastal Alaskan mountain goat (Oreamnos americanus). Using long-term GPS data and field observations across four populations (421 individuals over 17 years), we show that avalanches caused 23-65% of all mortality, depending on area. Deaths varied seasonally and were directly linked to spatial movement patterns and avalanche terrain use. Population-level avalanche mortality, 61% of which comprised reproductively important prime-aged individuals, averaged 8% annually and exceeded 22% when avalanche conditions were severe. Our findings reveal a widespread but previously undescribed pathway by which snow can elicit major population-level impacts and shape demographic characteristics of slow-growing populations of mountain-adapted animals.
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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 | 0.000 |
| 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) | 0.001 | 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".