HOW MOOSE SELECT FORESTED HABITAT IN GROS MORNE NATIONAL PARK, NEWFOUNDLAND
Bibliographic record
Abstract
ABStRACt: Current Parks Canada policy does not allow moose (Alces alces) to be hunted in Na-tional Parks in Newfoundland and Labrador; combined with the extirpation of wolves (Canis lupus), this policy creates a situation where introduced moose (A. a. americana) are relatively predator-free in Gros Morne National Park. Forested areas of this park are frequently disturbed by defoliating insects resulting in extensive young conifer forest; increasingly, more areas are identified as failing to regenerate to normal tree densities or “not sufficiently restocked ” (NSR). We used data from GPS-collared moose that occupy areas of the park where limited timber cutting is allowed for domestic purposes and a very detailed and current forest inventory exists; such areas are still dominated by insect and wind disturb-ance, including a large designation of NSR forest. We hoped to determine whether moose are found preferentially in disturbed forest versus other landscape patches during summer or winter, during day or night, and under certain temperature conditions. Variability in habitat availability and habitat use by moose appears to preclude forest management options directed at specific habitat types. ALCES VOL. 45: 125-135 (2009) Key words: Alces alces, absence of predators, Gros Morne National Park, moose, Newfoundland, overabundance, population dynamics, resource selection function.
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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) | 0.002 | 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".