Climate factors related to localized changes in snowshoe hare (<i>Lepus</i> <i>americanus</i>) occupancy
Bibliographic record
Abstract
Information on climate that influences snowshoe hares (Lepus americanus Erxleben, 1777) can inform adaptation strategies. We identified climate factors correlated with localized changes in occupancy of snowshoe hares in Michigan, USA. A change in occupancy occurred if a site (∼7.5 ha) knowingly occupied by hares sometime in the past became unoccupied. We used local ecological knowledge to map sites where hares historically occurred and to assign a year of last-known occupancy. At 134 historically occupied sites, we conducted snow track surveys in 2013 to determine current occupancy status. We identified climate variables having relevance to hare population demographics and modeled the likelihood that those variables influenced current occupancy status. The top-ranking model included maximum temperature from 15 May to 19 January; as maximum temperature increased, the likelihood of a site becoming unoccupied increased. The second-ranked model included total number of days with measurable snow on the ground; as days with snow on the ground decreased, the likelihood of a site becoming unoccupied increased. Our data indicated that site occupancy status of hares can be described by climate variables and that the southern edge of snowshoe hare distribution in Michigan shifted northward by ∼45 km over the last 20 years.
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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 | 0.001 | 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".