Core Areas of Habitat Use: The influence of spatial scale of analysis on interpreting summer habitat selection by moose (Alces alces
Bibliographic record
Abstract
ii I investigated summer habitat selection patterns within the home ranges of 60 GPS-collared adult female moose (Alces alces) in northwestern Ontario. I developed a model that identified the ‘summer ’ period for moose and I suggest and test a new approach for objectively delineating areas of intense use, or ‘core ’ areas. Once summer and core areas were established, I tested two competing hypotheses to identify differences in habitat selected between the core areas and home range peripheries; (1) core areas represent superior spatial configurations of habitats when compared to home range peripheries; and (2) core areas are selected to contain a subset of ‘preferred ’ forage species with higher individual densities or a higher total density of all forage species than home range peripheries. The study was conducted in 2 landscapes characterized by different disturbance patterns created by different timber harvesting systems: modified “guidelines” cut (MGC); and progressive, contiguous clear cut (PCC). Moose move more and faster during the summer than the winter to exploit available forage. I defined moose ‘summer ’ as the period during the calendar year when an animal maintains a rate of movement greater than the annual mean. Using a sub-sample (n=32) of animals collared in 2000, I determined 1 May 2000 as the median date for the ‘winter-summer’ transition (range: 2 April-24 May) and the median transition from ‘summer-winter ’ was 25
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".