Habitat factors affecting vulnerability of moose to predation by wolves in southeastern British Columbia
Bibliographic record
Abstract
We compared habitat features at sites where wolves (Canis lupus) killed moose (Alces alces), sites 500 m from kills, telemetry locations of moose, and random sites, to examine the influence of logging and other landscape features on the vulnerability of moose to predation by wolves in southeastern British Columbia during the winters of 1984-1985 through 1995-1996. Moose-kill sites were located farther from the edges of seedling and pole size-class patches than telemetry locations. Road density was lower and wolf use was higher in areas where kill sites occurred than in areas where relocation or random sites occurred. Kill sites were located at lower elevations than relocation or random sites. A logistic regression model using road density, elevation, distance from trails, and distance from size-class polygon edges successfully classified 94.5% of sites as either kills or locations. Moose density was greater and hiding-cover levels were lower at kill sites than at control sites. Forest harvest practices in this study area apparently did not increase the vulnerability of moose to wolf predation.
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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 | 0.000 |
| 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".