Keeping cool in the warming Arctic: thermoregulatory behaviour by Svalbard reindeer (<i>Rangifer tarandus platyrhynchus</i>)
Bibliographic record
Abstract
In animals with long generation times, evolution of physiological and morphological traits may not be fast enough to keep up with rapid climate warming, but thermoregulatory behaviour can possibly serve as an important buffer mitigating warming effects. In this study, we investigated if the cold-adapted Svalbard reindeer (Rangifer tarandus platyrhynchus (Vrolik, 1829)) used cool bed sites as a thermoregulatory behaviour in the summer. We recorded habitat variables and ground temperature at 371 bed sites with random “control” sites 10 and 100 m distant. Using case-control logistic regression, we found that reindeer selected bed sites on cool substrates (snow and mire), as well as cold, dry ground on days with warm ambient temperatures, while they avoided such sites on cold days. Selection of both cool substrates and cool ground did not depend on age or sex. The study was conducted in an environment where neither predatory threat nor insect harassment influenced bed site selection. Our findings suggest that the thermal landscape is important for habitat selection of cold-adapted Arctic ungulates in summer. Thus, behavioural strategies may be important to mitigate effects of climate change, at least in the short term.
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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 | 0.000 |
| 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) | 0.001 | 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".