MOOSE HABITAT PREFERENCES IN RESPONSE TO CHANGING AVAILABILITY
Bibliographic record
Abstract
Application of Habitat Suitability Index (HSI) models without testing in areas other than where they were generated, and claims that habitat preferences have been proven, indicate that managers and scientists believe that habitat preferences of wildlife are fixed. We tested this hypothesis by comparing habitat preferences of 2 groups of moose (Alces alces) in northeastern Alberta, Canada, to which the same habitat classes were available but differed in relative abundance. We estimated habitat availability for each of 22 radiomarked, adult female moose and divided the animals into 2 groups based on the similarity of relative habitat class abundances. We measured habitat preference for individual moose from each group during 2 seasons in each of 2 years using a simple resource selection function (RSF). We used analysis of variance (ANOVA) to compare differences between groups. Preference of several habitat classes differed between groups, indicating that habitat preferences of moose are not fixed and change as the relative abundance of available habitat changes. Managers must recognize and account for this concept in the application of habitat prescriptions or management plans.
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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".