Home range, movements, and denning chronology of the Grizzly Bear (<em>Ursus arctos</em>) in west-central Alberta
Bibliographic record
Abstract
An understanding of the natural history of the Grizzly Bear (Ursus arctos) is important for recovery planning. We present data on home range size, movements and denning chronology collected using Global Positioning System (GPS) collars on Grizzly Bears in west-central Alberta. Mean annual kernel estimates for adult (1034 ± 656 (SD) km2) and subadult (1298 ± 1207 km2) males were larger than those for females with cubs of the year (213 ± 212 km2) and lone adult females (337 ± 176 km2) but not different from sub-adult females, females with yearlings, or females with ≥ 2-yr old cubs (P > 0.05). Mean rates of movement among female age–reproductive classes were different from each other (Z9 < 2.70, P > 0.05) but not different from sub-adult males (Z9 < 2.70, P > 0.05). Rates of movement of adult males were significantly different only from those of females with cubs of the year (Z9 = 3.94, P = 0.001). The greatest amount of movement occurred in June and the least in October. Bears traveled fastest in the morning and evening and slowest at night. Pregnant females had the longest denning period (175 days, ± 16 days SD). No difference was detected in denning duration among the remaining five age–sex–reproductive classes (P > 0.05). GPS collars provided large location datasets from which accurate home range estimates, hourly movement rates, and precise denning dates were determined. Examining similarities and differences in the basic biology of Grizzly Bears from various locations will improve our understanding of the plasticity of this species and the potential impacts of habitat and climate change.
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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 | 0.001 | 0.001 |
| 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".