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 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".