Density, snow, and seasonality lead to variation in muskox (<i>Ovibos moschatus</i>) habitat selection during summer
Bibliographic record
Abstract
Understanding how environmental conditions influence habitat selection and suitability of free-ranging animals is critical, as the outcome may have implications for individual fitness and population dynamics. Density and snow are among the most influential environmental conditions driving habitat-selection patterns of northern ungulates. We used two decades of census data from high Arctic Greenland to quantify inter- and intra-annual variations in muskox (Ovibos moschatus (Zimmermann, 1780)) habitat selection and suitability during the Arctic summer (July through October). Across years, habitat selection varied considerably, and the strength of habitat selection appeared negatively related to both muskox density and spring snow cover. In early summer, habitat suitability was high and spatially rather uniform. Towards the autumn, suitable habitats contracted to just the lower elevations, when muskoxen exhibited increasingly stronger habitat selection towards low elevations and dense vegetation. This selection strategy clearly reflects the need to build up fat reserves for the upcoming winter, highlighting the energetic importance of the Arctic summer. Extreme climatic events such as freezing rain in autumn are increasing in frequency in Greenland and limit muskox access to high-quality forage in fens. Such events may therefore negatively affect the energy acquisition process of muskox with potential cascading consequences on population dynamics.
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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 | 0.001 |
| 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".