Spring food habits of Wolverine (<i>Gulo gulo</i>) in the Colville River watershed, Alaska
Bibliographic record
Abstract
Wolverines (Gulo gulo) are relatively abundant on the North Slope of Alaska, an Arctic ecosystem dominated by tundra habitats that run north from the Brooks Range across a wide coastal plain to the Beaufort Sea. The region contains a range of potential Wolverine prey species, including ungulates (Caribou [Rangifer tarandus], Moose [Alces americanus]), Arctic Ground Squirrel (Urocitellus parryii), and both Soricidae and Cricetidae species. The seasonal composition of these, and other prey species, in the Wolverines’ diet is not well understood. We collected Wolverine scats during spring (March–May) on the North Slope while tracking animals from snowmobiles and with helicopters that visited areas identified as of interest during ground surveys or using global positioning system collared animals. We analyzed prey remains in 48 scat samples based on hair, bone, and other prey fragments. We then calculated frequency of occurrence, percentage of occurrence, and weighted percent volume for each major prey category detected. We confirmed species identity of scats as Wolverine by amplifying the control region of the mitochondrial DNA. We estimated spring diet diversity and richness based on nine major prey categories detected in scats. Ungulates and cricetids together constituted 69% of the Wolverines’ spring diet, with Snowshoe Hare (Lepus americanus) constituting 9%, fox (Vulpes spp.) 6%, Arctic Ground Squirrel 2%, birds 2%, American Beaver (Castor canadensis) less than 1%, and unknown 6%.
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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 | 0.000 |
| 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 | 0.000 |
| 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".