Data from: Climate change increases predation risk for a keystone species of the boreal forest
Bibliographic record
Abstract
Abstract Canada lynx (Lynx canadensis) and snowshoe hares (Lepus americanus) form a keystone predator-prey cycle that has large impacts on the North-American boreal forest vertebrate community. Snowshoe hares and lynx are both well-suited for snowy winters, but climate change associated shifts in snow conditions could lower hare survival and alter cyclic dynamics. Using detailed monitoring of snowshoe hare cause-specific mortality, behaviour, and prevailing weather, we demonstrate that hare mortality risk is strongly influenced by variation in snow conditions. Although predation risk from lynx was largely unaffected by snow conditions, coyote (Canis latrans) predation increased in shallow snow. Maximum snow depth in our study area has decreased 33% over the last two decades and predictions based on prolonged shallow snow indicate future hare survival could resemble that seen during population declines. Our results indicate that climate change could disrupt cyclic dynamics in the boreal forest.
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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 | 0.001 | 0.001 |
| 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) | 0.009 | 0.002 |
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".