Spatially segregated foraging patterns of moose (<i>Alces</i> <i>alces</i>) and mountain hare (<i>Lepus</i> <i>timidus</i>) in a subarctic landscape: different tables in the same restaurant?
Bibliographic record
Abstract
Differences in body sizes of mountain hares (Lepus timidus L., 1758) and moose (Alces alces (L., 1758)) affect their ability to perceive and respond to environmental heterogeneity and plant density. Therefore, we expect these species to show niche separation at different scales in the same environment. Results showed that the numbers of mountain birches (Betula pubescens subsp. czerepanovii L.) browsed by moose per unit area was inversely related to hare browsing. Moose browsed larger birches compared with hares, and while hares targeted areas with high birch densities regardless of tree sizes, moose preferentially browsed areas with high densities of large birches. Moose browsing was clustered at spatial intervals of 1000–1500 m, while hare browsing was clustered at intervals of less than 500 m. Willows (genus Salix L.) in the study area were heavily browsed by moose, while few observations of hare browsing on willow were made. Regarding both hare and moose, numbers of birch stems with new browsing per sample plot were positively correlated with the numbers of birch stems with old browsing, indicating that hare and moose preferred the same foraging sites from year to year. These findings have implications for management of the species because they show the importance of scale and landscape perspectives in planning and actions.
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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".