Bioclimatic, terrain, and specific peatland composition are major drivers of woodland caribou winter habitat suitability in northern Ontario
Bibliographic record
Abstract
Mapping of winter habitat suitability is important for the persistence and conservation of at-risk woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)). While well documented at the national scale for boreal woodland caribou, particularly in highly disturbed southern ranges, winter habitat suitability remains understudied in northern and intact ranges such as in northern Ontario. We used boosted regression tree species distribution modeling and environmental variables with ecological relevance to woodland caribou to predict and map suitable winter woodland caribou habitat in northeastern Ontario, Canada. The best model suggests that peatland types and the climatic effect of James and Hudson Bay may have a marked (68.8% cumulative relative influence) effect on woodland caribou habitat suitability. Based on this, a predictive model identified a large and clustered zone of winter woodland caribou habitat centered within the transition between the ecozones. By accounting for local-scale aspects of woodland caribou habitat and bioclimatic variables, our model provides comprehensive predictions of woodland caribou winter habitat suitability in this transition zone. Additional investigation of the role of peatland type in woodland caribou habitat suitability in different seasons and different regions may help further understand woodland caribou distribution and habitat use.
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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 | 0.001 | 0.000 |
| 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".