Climatic variation and risk assessment in a highly seasonal mammal
Bibliographic record
Abstract
Abstract Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging, phenology, and migration. Although many studies focus on the impacts of phenological changes on physiology or fitness enhancing behaviors, fewer have investigated the relationship between variation in weather and phenology on risk assessment. Fleeing from predators is an economic decision that incurs costs and benefits. As environmental conditions change, animals may face additional stressors that affect their decision to flee and influence their ability to effectively assess risk. Flight initiation distance (FID)—the distance at which animals move away from threats—is often used to study risk assessment. FID varies due to both internal and external biotic and physical factors as well as anthropogenic activities. We asked whether variation in weather and phenology is associated with risk-taking in a population of yellow-bellied marmots (Marmota flaviventer). As the air temperature increased marmots tolerated closer approaches, suggesting that they either perceived less risk or that their response to a threat was thermally compromised. The effect of temperature was relatively small and was largely dependent upon having a larger range in the full data set that permitted us to detect it. We found no effects of either the date that snow disappeared or July precipitation on marmot FID. As global temperatures continue to rise, rainfall varies more and drought becomes more common, understanding climate-related changes in how animals assess risk should be used to inform population viability models.
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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) | 0.001 | 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".