Use of Whitebark Pine (<i>Pinus albicaulis</i>) seeds by GPS-collared Grizzly Bears (<i>Ursus arctos</i>) in Banff National Park, Alberta
Bibliographic record
Abstract
Seeds of Whitebark Pine (Pinus albicaulis) are a major food for Grizzly Bears (Ursus arctos) in the Yellowstone ecosystem. In Canada, Grizzly Bears are known to eat Whitebark Pine seeds, but little additional information, such as the extent of such use and habitat characteristics of feeding sites, is available. Because Grizzly Bears almost always obtain Whitebark Pine seeds by excavating cones from persistent caching sites (middens) made by Red Squirrels (Tamiasciurus hudsonicus), it is possible to infer Whitebark Pine feeding when bears are located near excavated middens in Whitebark Pine stands. During 2013–2018, I conducted a retrospective study in Banff National Park using data from 23 Grizzly Bears equipped by Parks Canada staff with global positioning system (GPS) collars. My objectives were to use GPS fixes to determine the percentage of these bears that had been located in close proximity to excavated middens containing Whitebark Pine seeds and to describe the habitat at these excavated middens. I linked 15 bears (65%) to excavated middens and, by inference, consumption of Whitebark Pine seeds. Excavated middens occurred on high-elevation (mean 2103 ± 101 [SD] m), steep (mean 26° ± 8°) slopes facing mostly (96%) north through west (0–270°). Use of Whitebark Pine seeds by at least 65% of the 23 studied Grizzly Bears suggests that conservation of Whitebark Pine in Banff National Park would concomitantly benefit the at-risk population of 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".