Carnivore-specific stable isotope variables and variation in the foraging ecology of modern and ancient wolf populations: case studies from Isle Royale, Minnesota, and La Brea
Bibliographic record
Abstract
We use carbon and nitrogen isotope data collected from two North American gray wolf ( Canis lupus L., 1758) populations (Isle Royale and northern Minnesota) to both calculate carnivore-specific isotopic variables and investigate wolf foraging ecology. The isotopic enrichments of13C and15N that occur between mammalian carnivores and their prey have not been well defined in modern populations. We use bone collagen from the Isle Royale National Park wolf, moose ( Alces alces (L., 1758)), and beaver ( Castor canadensis Kuhl, 1820) populations to determine trophic enrichment factors of 1.3‰ ± 0.6‰ for δ13C and 4.6‰ ± 0.7‰ for δ15N. We apply these carnivore-specific fractionation factors to a case study from the fossil record, and reconstruct the diets of late-Pleistocene dire wolves ( Canis dirus (Leidy, 1858)) from the La Brea tar pits. We use the Minnesota wolf tissue (collagen, hair, muscle) isotopic data to estimate carnivore population subsample sizes needed to replicate the mean values of the whole population within one standard deviation. Finally, we compare the Isle Royale and Minnesota collagen and hair isotopic data to published δ13C and δ15N values for North American gray wolf populations. We find that interpopulation differences in isotope variances provide insight into wolf foraging ecology.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".