Grizzly bear diet shifting on reclaimed mines
Bibliographic record
Abstract
Industrial developments and reclamation change habitat, possibly altering large carnivore food base. We monitored the diet of a low-density population of grizzly bears occupying a landscape with open-pit coal mines in Canada. During 2009–2010 we instrumented 10 bears with GPS radiocollars and compared their feeding on reclaimed coal mines and neighboring Rocky Mountains and their foothills. In addition, we compared our data with historical bear diet for the same population collected in 2001–2003, before extensive mine reclamation occurred. Diet on mines (n=331 scats) was dominated by non-native forbs and graminoids, while diets in the Foothills and Mountains consisted primarily of ungulates and Hedysarum spp. roots respectively, showing diet shifting with availability. Field visitation of feeding sites (n=234 GPS relocation clusters) also showed that ungulates were the main diet component in the Foothills, whereas on reclaimed mines bears were least carnivorous. These differences illustrate a shift to feeding on non-native forbs while comparisons with historical diet reveal emergence of elk as an important bear food. Food resources on reclaimed mines attract bears from wilderness areas and bears may be more adaptable to landscape change than previously thought. The grizzly bear’s ready use of mines cautions the universal view of this species as umbrella indicative of biodiversity.
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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 | 0.001 | 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".