Feeding-crater selection by high-arctic reindeer facing ice-blocked pastures
Bibliographic record
Abstract
Increased frequency of ground-icing events is likely to influence population dynamics in arctic ungulates, but their behavioural responses remain unexplored. During a record-mild winter with heavy rainfall, we analysed snow and ice characteristics and foraging trade-offs by Svalbard reindeer ( Rangifer tarandus platyrhynchus Vrolik, 1829) on a semi-isolated, recently occupied range. Snow depths were well within thresholds for cratering, but >90% of low altitudes was covered by a thick ice coat on the ground (median thickness 9 cm). Different strategies to cope with these conditions appeared. Part of the population sought mountainous habitat with very sparse vegetation. Individuals remaining at lower altitudes either used sparsely vegetated, wind-blown ridges partially covered with ice, or apparently applied olfactory senses to locate vegetation in ice-free microhabitat beneath the snowpack. No feeding craters were covered by ground ice, compared with most nearby controls. Following ground-ice avoidance, vegetation rather than snowpack properties determined fine-scale crater selection. Even under such poor conditions, the presence of medium- to high-quality forage (dwarf willow ( Salix polaris Wahlenb.) and fruticose lichens) rather than low-digestible, high-biomass forage (mosses) influenced cratering decisions. Behavioural plasticity combined with a gradually depleted lichen resource can partly buffer the reindeer against predicted climate change, at least in the short-term.
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How this classification was reachedexpand
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) | 0.004 | 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 teacher head, 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".