Using stable isotopes to define diets of wolves in northern British Columbia, Canada
Bibliographic record
Abstract
Wolves (Canis lupus), as both opportunistic and specialist predators, can limit and regulate ungulate dynamics. As part of understanding predator–prey interactions in the largely undisturbed system of the Besa-Prophet area in northern British Columbia, we used stable isotopes of carbon and nitrogen to infer seasonal diets of 5 wolf packs. We selected the hair, tissue, or blood sample of each prey species that could best index within-season diet composition. Seasonal isotopic differences for a given sample type were as much as 0.28‰ δ13C and 0.97‰ δ15N. The large biomass species of moose (Alces americanus) and elk (Cervus elaphus) dominated the diets of wolves, but caribou (Rangifer tarandus) and Stone's sheep (Ovis dalli stonei) also were locally or seasonally important to some packs. Mean isotopic determinations of summer food habits were correlated positively (P < 0.001) with proportions of prey by species determined from scat samples. This general agreement lends support for the tissue to diet discrimination values used in the Bayesian modeling and indicates that the longer-term dietary estimates from stable isotopes were reflective of shorter-term recent ingestion. Although moose have been assumed to be the most important prey item for wolves throughout the year in northern British Columbia, our results indicate that dietary dynamics of wolves in the Besa-Prophet area are more complex than previously reported.
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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) | 0.001 | 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".