Space use of moose in relation to food availability
Bibliographic record
Abstract
We assessed the influence of temporal and spatial changes in food availability on home-range size and movements of moose (Alces alces (L., 1758)). Ten adult moose were radio-collared and monitored with GPS telemetry collars during two contrasting time periods based on food quality and availability: summer (growing period) and winter (dormant and snowy period). According to the habitat productivity – home-range size hypothesis, we expected home-range size and movement rate to be negatively correlated with the proportion of food-rich habitat types in home ranges. Food availability influenced space use, but the effect was more pronounced at the small scale (i.e., movement rates) than at the large scale (i.e., home-range sizes) and during winter than summer. The negative relationship between food availability and home-range size only existed in winter, but movement rates were lower in food-rich habitat types in both time periods. Our results suggested that body size was not the primary factor governing sex-related differences in space use which were likely more related to specific needs of females with regard to the feeding and protection of calves. We suggest that the habitat productivity – home-range size hypothesis may not always be supported when forage is widely distributed and that movement rates can be a better index of forage availability than home-range sizes in such situations.
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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".