Seasonal resource selection of woodland caribou (<i>Rangifer tarandus caribou</i>) across a gradient of anthropogenic disturbance
Bibliographic record
Abstract
Resource selection functions are useful tools for land-use planning, especially for wide-ranging species with sensitivity to anthropogenic disturbance. We evaluated five a priori hypotheses describing seasonal habitat selection of woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) across three regions in northern Ontario. Two regions were Boreal Shield dominated, one area with relatively high anthropogenic disturbance (due to commercial forestry) and the other with relatively low anthropogenic disturbance. The final region was located on the wetland-dominated Hudson Bay Lowlands. Each region encompassed two caribou management ranges: one was used for model development and the other for model evaluation. We developed seasonal resource selection probability functions using seasonal utilization distributions and isopleths derived from GPS collar data (from 212 caribou) to identify high- and low-use areas. We explored selection across five spatial scales; selection patterns were strongest at the 10 000 ha scale. We found temporal and spatial variations in all environmental predictors across ranges and seasons, especially in the Hudson Bay Lowlands. Our results consistently supported the integrated global model (with common variables but range-specific coefficients) where caribou habitat use is related to minimizing apparent competition with moose (Alces alces (L., 1758)) while avoiding disturbed areas, and utilizing areas with adequate forage.
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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".