Bioenergetic Prediction of Climate Change Impacts on Northern Mammals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 physiological 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 it