Factors Influencing the Abundance of Berry Plants for Black Bears, <em>Ursus americanus</em>, in Quebec
Bibliographic record
Abstract
Berries generally represent the primary food source used by Black Bears (Ursus americanus) during summer and sometimes fall. Our study attempted to identify factors influencing the abundance of these critical resources for Black Bears in Quebec. We used data from the ecological inventories of the Ministère de l’Environnement du Québec. Three different study areas were selected, including the Papineau-Labelle Wildlife Reserve (326 sample plots; 24 species), the Laurentides Wildlife Reserve (679 sample plots; 16 species) and the Côte-Nord administrative region (1944 sample plots; 30 species). Each site represented a different bioclimatic domain. Characteristics of the dominant vegetation (density and height), soil texture, and drainage classes (vertical or oblique) were used as analysis factors. In general, height and density of the dominant vegetation had a significant effect on the abundance of berry plants. Berry plants were more abundant in plots where dominant vegetation height and density were low. Soil texture had little effect on the abundance of berry plants, and the influence of drainage varied depending on the region. This study allowed us to determine the optimal sites of berry resources for Black 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".