Influences on the balance between energetics and risk in snowshoe hares
Bibliographic record
Abstract
Animals balance acquiring food for energy while minimizing their risk of predation, but climate or habitat features can tip the scales. I used snowshoe hare (Lepus americanus) to test how external variables influence foraging and survival. First, I used winter coat-colour mismatch to test the Camouflage Hypothesis. I predicted that white hares mismatched on a snowless winter background would be more vulnerable to predation than matched hares. Instead, mismatched hares had lower mortality risk than matched hares. Temperature and snow depth, not mismatch, influenced survival and foraging behaviour. Next, I tested the Risk Allocation Hypothesis with a novel risk simulation experiment in the context of spring environmental factors. Hares did not allocate risk based on simulated risk or weather conditions. Instead, hares were risk averse as a function of canopy cover. Taken together, my results demonstrate that multiple mechanisms likely impact the energetics-risk balance. Snow depth and canopy cover have direct implications on predation risk, while temperature impacts energetics; however, effects may not be detectable if conditions do not represent an energetic challenge. My work highlights the idiosyncrasies of place considering previous results across the hare range, and contributes to mismatch and behavioural frameworks for a seasonally coat-colour changing 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".