Fat dynamics and development of body condition indices for harvested populations of wolverine <i>Gulo gulo</i>
Bibliographic record
Abstract
Sufficient energy reserves are crucial to the overwinter survival of northern non‐hibernating mustelids. We sought a reliable index of body condition (fatness) in harvested populations of wolverine Gulo gulo , based on the relationship between fatness and the mass of distinct fat depots extractable by necropsy. Fatness did not differ significantly between genders or winter months, nor was it significantly related to body size or age. Using a first group of 18 males and 14 females, we developed predictive least‐square linear regressions between fat depots (popliteal, sternal, omentum, mesenteric and perirenal) and fatness (g fat/100 g body mass) using skinned carcasses provided by fur trappers in the Yukon, Canada. Fatness was consistently better predicted in females than in males. Fatness was best predicted by the sternal fat depot (R 2 = 0.73) in males and by the omentum as well as sternal fat depots in females (R 2 = 0.94 and 0.87, respectively). We then compared known fatness and fatness predicted from regressions of the sternal fat depot using a second group of 14 males and nine females, and mean fatness did not differ significantly. We suggest that, due to its ease of extraction and predictive power, the sternal fat depot is a valid fat index with both sexes of wolverine, although it (or any other fat depot) should be used with caution with males, which seem more prone to obesity. This new index will help wildlife managers monitor changes in body condition of wolverines in response to changes in environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".