Blackwell Publishing, Ltd. starvation, stress, adaptation or artefact?
Bibliographic record
Abstract
1. Overwinter mass loss can reduce energetic requirements in mammals (Dehnel’s phenomenon). Alternatively, mass loss can result from food limitation or high predation risk. 2. We use data from fertilizer, food-supplementation and predator-exclusion experiments in the Yukon during a population cycle from 1986 to 1996 to test the causes of overwinter mass loss by snowshoe hares (Lepus americanus). In all years, some hares on control sites gained mass overwinter. During the increase phase the majority gained mass, but in all other phases the majority lost mass. 3. Snowshoe hares weighing < 1000 g in autumn always gained mass overwinter, as did the majority that weighed 1000–1400 g. Hares weighing> 1800 g in autumn usually lost mass. 4. Snowshoe hares on the predator-exclosure + food site gained mass overwinter in all years. Hares on the food-supplementation sites lost mass during the decline but gained mass in all other phases. Fertilization had little effect on mass dynamics. 5. Snowshoe hares were more likely to lose mass during winters with low survival rates. Snowshoe hares on the predator-exclosure treatments were more likely to gain mass than were hares on control sites.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.172 | 0.122 |
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".