Multiscale habitat selection by cow moose (<i>Alces</i><i> alces</i>) at calving sites in central Ontario
Bibliographic record
Abstract
There is limited knowledge of moose (Alces alces (L., 1758)) calving site selection at the southern limit of their range. Varying results from previous research on calving habitat selection make it challenging to extrapolate to other populations. We used a combination of global positioning system (GPS) data from collared cow moose and GPS locations of expelled vaginal implant transmitters and neonatal calf captures to identify calving sites in two areas of central Ontario, Canada (Algonquin Provincial Park and Wildlife Management Unit 49 (WMU49)), that differed in terms of moose and timber harvest management. We investigated selection and avoidance of habitat types, roads, topography (slope and elevation), and forest stands of varying successional age during the calving season at three spatiotemporal scales — annual home range, seasonal range, calving site — using a combination of distance-based and classification-based variables. In both study areas, calving sites were on gentler slopes and closer to conifer stands than expected at the fine scale. Cows in WMU49 strongly selected rock–grass sites across all scales. This study also demonstrates the feasibility of using GPS collars to infer parturition and location of calving sites. We recommend ground-based microhabitat data be collected to better understand habitat selection of moose during calving.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".