MOOSE HABITAT USE THROUGHOUT GROS MORNE NATIONAL PARK
Bibliographic record
Abstract
ABSTRACT: Previous research indicated high variability in availability and habitat use by female moose in the lowlands of Gros Morne National Park (GMNP), Newfoundland and Labrador, an area dominated by bogs and forest. Here, we extend the earlier analysis with an additional 7 female moose (Alces alces americana) occupying the Park highlands, a region dominated by heath and shrub vegeta-tion with forest limited to sheltered valleys, typical of interior and highland parts of the province. Resource selection function (RSF) models with differences in habitat use between moose resident in the 2 regions and 2 moose that migrated from the lowlands in winter to the highlands in summer were rejected. In summer, more use of closed-canopy forest types occurred on the lowlands, while more use of non-forest habitat types occurred on the highlands. As before, we found that selection of disturbed forest is a winter phenomenon on the lowlands of GMNP; the same series of habitat types associated with disturbance were avoided in summer. Summer migration by about 20 % of GMNP moose to the highlands suggests that foraging opportunities are better during that season than in winter, a motivation for migration perhaps augmented by an overabundance of moose on the lowlands and unfavourable temperatures in disturbed areas that might otherwise serve as lowland foraging areas. An observation of more clustered relocations of moose on the highlands than on the lowlands of
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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".