Linking Wolf Diet to Changes in Marine and Terrestrial Prey Abundance
Bibliographic record
Abstract
Since most wolf (Canis lupus) diet studies have been conducted in inland ecosystems, comparatively few data are available on diets of wolves in coastal systems. We investigated the diet of wolves in Glacier Bay, Alaska, from 12 May to 28 June in both 2010 and 2011. Although we identified 12 different prey species, including birds and small to medium-sized mammals, in wolf scats, moose (Alces alces) was the most frequent food item, observed in 80% of all scats. In contrast, a study conducted in 1993 in an area 37 km away found harbor seal (Phoca vitulina richardii) in 41% of wolf scats. Although we cannot account for differences in sampling design between the two studies, wolf diets may have changed between the two time periods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".