Ingestion of fat tissue from wolf prey species and its influence on fatty‐acid composition in sled dogs
Bibliographic record
Abstract
ABSTRACT We conducted feeding experiments on Canadian Inuit sled dogs ( Canis familiaris borealis ) as a surrogate for wolves ( Canis lupus ) to examine whether fatty‐acid signatures could be used to estimate relative intake of common prey in the Great Lakes area, USA. We obtained fat tissue from white‐tailed deer ( Odocoileus virginianus ), moose ( Alces alces ), beaver ( Castor canadensis ), and domestic cow ( Bos spp.), and provided 8 treatments of prescribed proportions to 6–7 dogs/treatment (total 50 dogs) over a period of 60 days. Pre and post‐treatment fat‐tissue samples from dogs were collected by biopsy to examine fatty‐acid composition. Approximately 10 60‐mg samples of fat from each prey species were analyzed using gas chromatography to develop a database of fatty‐acid signatures for each prey species. Fatty‐acid signatures of these 4 prey species were distinct. Fatty‐acid signatures of sled dogs changed with diet consistent with prescribed treatments. However, we could not reliably reconstruct their actual diet using Iverson et al.'s (2004) model. This study demonstrates that analysis of both prey and predator's fatty‐acid composition may be an innovative method to estimate dietary history of a terrestrial predator, such as wolves, but additional work is needed to make dietary predictions. © 2013 The Wildlife Society
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".