What factors determine cyclic amplitude in the snowshoe hare (<i>Lepus</i><i>americanus</i>) cycle?
Bibliographic record
Abstract
Snowshoe hares (Lepus americanus Erxleben, 1777) fluctuate in 9–10 year cycles throughout much of their North American range. These cycles show large variations in cyclic amplitude and we ask what factors could cause amplitude variation. We gathered data from 1976 to 2012 on hare numbers in the boreal forest of Alaska, Yukon, Northwest Territories, and northern British Columbia to describe the amplitude of hare fluctuations and to evaluate four possible causes. First, weather could cause variation in amplitude via hare reproduction or survival, but this mechanism does not fit our data. Second, bottom-up processes involving forest succession could explain amplitude variation through changes in winter forage availability, but succession is too slow a variable in our study areas. Third, plant defenses entrained by hare over-browsing in one cycle can produce variation in plant quality and quantity in subsequent cycles. A mathematical model suggests this is a possible explanation. Fourth, predator recovery following the cyclic low is inversely related to hare cyclic amplitude, and the existing data are consistent with this mechanism. A standardized regional monitoring program is needed to improve our understanding of cyclic amplitude variation in hares and the possible role of predators and winter foods in affecting amplitude.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".