Grizzly bear use of avalanche chutes in the Columbia Mountains, British Columbia
Bibliographic record
Abstract
I examined spring season use of avalanche chutes by grizzly bears (Ursus arctos L.) in the Columbia Mountains, southeastern British Columbia. Sixty radio-collared grizzly bears were monitored between 1994 and 1998. The frequency of avalanche chute use, the selection of general habitat characteristics within avalanche chutes, and the selection of specific feeding and bedding sites within avalanche chutes by grizzly bears were documented. Fifty-four percent (366/672) of all grizzly bear radio-locations during the spring season (May 1 to July 31) were in avalanche chutes. The proportion of radio-locations in avalanche chutes for the 37 grizzly bears that accounted for > 10 spring season radiolocations each ranged between 20% and 90% (x = 56% ± 18% [mean ± SD]). This variation was not attributable to differences in use between sex or age classes. Within avalanche chutes, grizzly bears selected east and south aspects and areas dominated by grasses and forbs with minimal shrub abundance. Grizzly bears avoided very steep slopes but used all elevational parts of avalanche chutes - upper start zones, tracks, and lower runout zones. These patterns appeared to be tied to feeding site selection, because evidence of feeding was found at most telemetry locations investigated on the ground. Grizzly bears selected feeding sites on the basis of forage value and visual cover. Most feeding sites were characterized by high forage value and low visual cover, but weak positive interaction between these two factors indicated that grizzly bears also selected feeding sites with slightly lower forage values but high visual cover. Bed sites were found both in forest adjacent to avalanche chutes and directly within avalanche chutes. All bed sites found in forests adjacent to avalanche chutes were < 25 m from the forest / avalanche chute edge. The impact on grizzly bear use of avalanche chutes by two timber harvest activities was also examined. Grizzly bears avoided areas within avalanche chutes that were adjacent to cutblocks, possibly due to the removal of escape cover. In contrast, grizzly bears selected areas close to logging roads. Most logging roads traversing avalanche chutes in the study area had minimal vehicle traffic and were often situated close to areas with abundant food resources. I present suggestions for managing this important spring season habitat for grizzly bears.
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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 | 0.001 | 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".