Habitat selection by moose (Alces alces) in clear-cut landscapes.
Why this work is in the frame
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Bibliographic record
Abstract
Habitat selection by moose was studied over 4 years in two large sectors subject to intensive forest harvesting using a two-scale approach. At the coarser scale, i.e. location of the home range within the landscape, habitat selection did not appear to be influenced by the presence of clear-cuts. In one sector, moose preferred mature mixed stands, young coniferous, and mature coniferous stands. In the second sector, the highest preference was noted for cut areas and mature deciduous stands. Moose home ranges were located in areas with higher edge and interspersion among habitat patches. Home range size for females was positively related to the proportion of cuts, but movements were not. Habitat selection was more pronounced at the finer scale (animal locations within home range) and did not differ between sectors. Mixed stands were preferred in all seasons. Mature conifer stands were preferred in summer and in early winter while young conifer stands were preferred in late winter. Clear-cuts were avoided except in early winter. Moose were located in areas closer to edge between food and cover stands than were random locations, especially in late winter. A marked decrease in movements also was noted in late winter. This study shows differences in habitat selection pattern between the coarser and finer scales. For example, clear-cuts did not seem to markedly influence home range location at a coarser scale, and adaptations to minimize their impact seemed to operate at a finer scale. Coarser scale habitat selection was probably linked to a trade-off between predator avoidance and browse availability, whereas seasonal changes suggest behavioural adaptations of moose to maximize energy gain and counteract predation and other adverse environmental conditions at the finer scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
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 it