Gray Wolves (<i>Canis lupus</i>) consume free-ranging horses (<i>Equus ferus caballus</i>) on the Chilcotin plateau, British Columbia
Bibliographic record
Abstract
We analyzed 122 Gray Wolf (Canis lupus) scats, collected at all seasons during 2013–2017, to determine what wolves were eating in two adjacent study areas of the Chilcotin region, British Columbia: Brittany Triangle and Nemiah Valley. Free-ranging horses (Equus ferus caballus), Mule Deer (Odocoileus hemionus), Moose (Alces americanus), and small mammals contributed to wolf diet throughout the year. In both study areas, horse appeared more frequently than other species in occurrence-per-faeces (OF) and occurrence-per-item (OI) analyses. Horse occurred in 58 of 97 wolf scats from Brittany (OF 59.8%, OI 52.7%), deer in 26 (OF 26.8%, OI 23.6%), small mammals in 17 (OF 17.5%, OI 17.3%), Moose in four (OF 4.1%, OI 3.6%), and bird and fish minimally (both OF <2.5%, OI <2.5%). The sample size in the more human-developed Nemiah Valley was too small to estimate reliable patterns, but results suggest a similar ranking of dietary items. Domestic Cattle (Bos taurus), available in both study areas, appeared infrequently (combined area OF <3.5%, OI <3.0%). Based on our scat findings, free-ranging horses were a regular dietary item for wolves in the area. Studies elsewhere have found that, where wolves and free-ranging horses are sympatric, a predator–prey relationship exists.
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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 | 0.002 |
| 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) | 0.002 | 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".