SPACE USE AND MOVEMENTS OF MOOSE IN MASSACHUSETTS: IMPLICATIONS FOR CONSERVATION OF LARGE MAMMALS IN A FRAGMENTED ENVIRONMENT
Bibliographic record
Abstract
Moose (Alces alces) have recently re-occupied a portion of their range in the temperate deciduous forest of the northeastern United States after a >200 year absence. In southern New Eng- land, moose encounter different forest types, more human development, and higher temperatures than in other parts of their geographic range in North America. We analyzed seasonal minimum con- vex polygon home ranges, utilization distributions, movement rates, and home range composition of GPS-collared moose in Massachusetts. Seasonal home range sizes were not different for males and females and were within the range reported for low latitudes elsewhere in North America. Seasonal movement patterns reflected the seasonal changes in metabolic rate and the influence of the species' reproductive cycle and weather. Home ranges consisted almost entirely of forested habitat, included large amounts of conservation land, and had lower road densities as compared to the landscape as a whole, indicating that human development may be a limiting factor for moose in the region. The size and configuration of home ranges, seasonal movement patterns, and use relative to human devel- opment have implications for conservation of moose and other wide-ranging species in more highly developed portions of their ranges. ALCES VOL. 49: 65-81 (2013)
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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".