Distribution and density of moose in relation to landscape characteristics: effects of scale
Bibliographic record
Abstract
We analyzed the relation between early winter distribution and density of female moose (Alces alces L.) and habitat heterogeneity in interior Alaska. We tested for effects of vegetation type, topography, distance to rivers and towns, occurrence and timing of fire, and landscape metrics. A spatial linear model was used to analyze effects of independent variables organized at multiple scales. Because densities of moose vary widely as a result of differences in management and other factors, a spatial response surface of the log of moose density was fit to remove large-scale effects. The analysis revealed that the densest populations of moose occurred closer to towns, at moderate elevations, near rivers, and in areas where fire occurred between 11 and 30 years ago. Furthermore, moose tended to occur in areas with large compact patches of varied habitat and avoided variable terrain and nonvegetated areas. Relationships of most variables with moose density occurred at or below 34 km 2 , suggesting that moose respond to environmental variables within a few kilometres of their location. The spatial model of density of moose developed in this study represents an important application for effective monitoring and management of moose in the boreal forest.
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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 | 0.002 |
| 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".