RELATIONSHIP BETWEEN BODY CONDITION AND VULNERABILITY TO PREDATION IN RED SQUIRRELS AND SNOWSHOE HARES
Bibliographic record
Abstract
We examined the relationship between physical condition and vulnerability to predation in 2 species of mammals having different life history traits. We predicted that predators would be more likely to kill substandard individuals disproportionately in red squirrels (Tamiasciurus hudsonicus) than in snowshoe hares (Lepus americanus), given that sciurids apparently are less susceptible to predation. We also predicted that differences would exist between patterns of substandard-prey selection among groups of predators of red squirrels but not among predators of snowshoe hares because of differential predator efficiencies in capturing elusive prey. Through radio tracking, we found that substandard squirrels (n = 113) were disproportionately vulnerable to predation, whereas hares (n = 125) were killed irrespective of condition. Although the relationship between condition and vulnerability to predation, relative to predator groups, did not differ significantly for either species of prey, the difference was qualitatively greater in squirrels than in hares. These results support the notion that predators are more likely to kill substandard individuals disproportionately when targeting prey species that are difficult to capture and, moreover, that the tendency for particular species of predators to take substandard prey may be a reflection of the predator's hunting strategy and efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".