Late-Winter Habitat Use by Mule Deer, <em>Odocoileus hemionus</em>, in Central Interior British Columbia
Bibliographic record
Abstract
In central interior British Columbia, extensive cut blocks to recover timber killed by the Mountain Pine Beetle (Dendroctonus ponderosae) could impact negatively on Mule Deer (Odocoileus hemionus) late-winter habitat. This study assessed the possibility of predicting the distribution of potential late-winter habitat for Mule Deer with the BC Vegetation Resources Inventory (VRI) dataset used to produce forestry maps. On the basis of literature review and roadside inventories in December 2004, I predicted that high-quality Mule Deer late-winter habitat would correspond to mature and old conifer-dominated stands with ≤ 20% deciduous species, a canopy closure ≥ 45%, tree heights ≥ 23 m, tree diameter at breast height ≥ 24 cm, and basal area ≥ 45 m2/ha, and would be located on < 60% slopes on south, southeast, southwest or west aspects, or on flat ground. I allocated weight values to these criteria to classify map polygons into high-, medium- and low-quality polygons, and produce predictive maps of late-winter habitat use by Mule Deer. I tested my predictive habitat rating by snowtracking along 18 km of transects in February-March 2006, and 15.6 km of transects in February 2007. I recorded 31 and 12 Mule Deer tracks in 2006 and 2007, respectively, all in high-quality polygons. The observed frequency of tracks per polygon type was significantly (P < 0.001) different from expected. All tracks were in mature and old conifer-dominated stands including 10-60% Lodgepole Pine (Pinus contorta) and 10-20% Trembling Aspen (Populus tremuloides). This study showed that it was possible to predict the distribution of potential late-winter habitat for Mule Deer using a series of habitat criteria and the VRI dataset. The extensive harvesting of Lodgepole Pine in mixed coniferous stands will undoubtedly have a negative impact on Mule Deer late-winter habitat quality and quantity. The rating of habitat types developed in this study should be used in forest management plans to determine sites that should be protected from logging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".