Verification of a Forest Rating System to Predict Fisher, <em>Martes pennanti</em>, Winter Distribution in Sub-boreal Forests of British Columbia, Canada
Bibliographic record
Abstract
This study verified the ability of a forest rating system to predict the winter distribution of Fisher (Martes pennanti) in the Sub-boreal Spruce Biogeoclimatic Zone of central interior British Columbia. Forest polygons (i.e., homogenous areas with similar forest stand characteristics) were classified according to their age and structural development, canopy closure, basal area in mature trees, average tree diameter at breast height, and percentage of shrub cover. Approximately 170 km of transects randomly distributed across polygons were inventoried (snowshoed) from December to February 2005-2008. A total of 278 Fisher tracks were recorded. The observed frequency of Fisher tracks per polygon type was significantly (P < 0.05) different from expected. The majority (245 or 88.1%) of tracks were recorded in excellent- and high-quality polygons corresponding mostly to mixed coniferous stands. On average, these stands were 138.2 years old, and had 54.4% canopy closure, 38.1 m2/ha basal area, 27.8 cm dbh, and 11.4% shrub cover. This study showed that the forest rating system was adequate to predict Fisher winter distribution, and could be used to develop forest management plans that are compatible with the species habitat requirements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".