Bioenergetic prediction of climate change impacts on northern mammals. Integrative Comparative Biology 44
Bibliographic record
Abstract
SYNOPSIS. Climate change will likely alter the distribution and abundance of northern mammals through a combination of direct, abiotic effects (e.g., changes in temperature and precipitation) and indirect, biotic effects (e.g., changes in the abundance of resources, competitors, and predators). Bioenergetic approaches are ideally suited to predicting the impacts of climate change because individual energy budgets integrate biotic and abiotic influences, and translate individual function into population and community outcomes. In this review, we illustrate how bioenergetics can be used to predict the regional biodiversity, species range limits, and community trophic organization of mammals under future climate scenarios. Although reliable prediction of climate change impacts for particular species requires better data and theory on the physio-logical ecology of northern mammals, two robust hypotheses emerge from the bioenergetic approaches presented here. First, the impacts of climate change in northern regions will be shaped by the appearance of new species at least as much as by the disappearance of current species. Second, seasonally inactive mammal species (e.g., hibernators), which are largely absent from the Canadian arctic at present, should undergo substantial increases in abundance and distribution in response to climate change, probably at the expense of continuously active mammals already present in the arctic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".