Pack Size of Wolves, <em>Canis lupus</em>, on Caribou, <em>Rangifer tarandus</em>, Winter Ranges in Westcentral Alberta
Bibliographic record
Abstract
We studied pack size of Wolves (Canis lupus) on Woodland Caribou (Rangifer tarandus caribou) winter ranges in westcentral Alberta. These Caribou winter ranges are experiencing increasing pressure from resource extraction industries (forestry, energy sector) and concerns have been raised regarding increased Wolf predation pressure on Caribou in conjunction with landscape change. Thirty-one Wolves, from eight Wolf packs, were fitted with radiocollars on two Caribou winter ranges in the Rocky Mountain foothills, near Grande Cache, Alberta (2000-2001). There was a mean of 8.2 Wolves/pack and between 30 and 39 Wolves on each of the RedRock/Prairie Creek and Little Smoky Caribou ranges. The average pack size of Wolves in this region does not appear to have increased over that recorded historically, but the range (5-18) in the number of Wolves per pack varied considerably over our study area. Wolves preyed predominately on Moose (Alces alces), averaging one Moose kill every three to five days. There was some indication that pack size was related to prey size, with the smallest pack preying on Deer (Odocoileus spp.). It was clear that Caribou could not be the primary prey for Wolves, due to their low numbers, and relative to the pack size and Wolf kills we observed.
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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 | 0.000 |
| 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 | 0.000 | 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".