Grizzly bears and forestry II. Distribution of grizzly bear foods in clearcuts of
Bibliographic record
Abstract
We assessed the occurrence and fruit production of 13 grizzly bear foods in west-central Alberta, Canada, to better understand use of clearcuts by grizzly bears. Comparisons were made between clearcuts and upland forest stands, while specific models describing food or fruit occurrence within clearcuts were developed from canopy, clearcut age, scarification, and terrain-related variables using logistic regression. Ants, Equisetum spp., Hedysarum spp., Taraxacum officinale, Trifolium spp., and Vaccinium myrtilloides occurred with greater frequency in clearcuts, while V. caespitosum, V. membranaceum, and V. vitis-idaea were more likely to occur in upland forests. No differences were evident for Arctostaphylos uva-ursi, Heracleum lanatum, Shepherdia canadensis, and ungulate pellets, an indicator of ungulate abundance. Mechanical scarification negatively impacted the occurrence of A. uva-ursi, Hedysarum spp., and S. canadensis, while weaker effects were apparent for ants and ungulate pellets. In contrast, the occurrence of Taraxacum officinale and Trifolium spp. were greater in scarified clearcuts. Age of clearcut or canopy cover was well correlated with the occurrence of most foods. For some species, however, terrain-derived variables predicted occurrence best. Fit and model classification accuracy using independent data proved good for most species. Patterns of fruit occurrence were related to canopy cover, with little support for other environmental covariates. In total, average fruit production for six fruit-bearing species was estimated at 22.9 kg/ha for clearcuts and 32.3 kg/ha for forests, a non-significant difference and generally less than that reported elsewhere in grizzly bear range. V. caespitosum and V. membranaceum
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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".