Assessing the utility of lidar remote sensing technology to identify mule deer winter habitat
Bibliographic record
Abstract
Winter habitat for mule deer (Odocoileus hemionus) is a critical concern throughout interior British Columbia, Canada. In winter, mule deer require a food source of twigs and woody browse and face significant winter snow cover. A range of studies have established that good winter range for mule deer reduces the impact of a negative energy balance by providing adequate food, good vegetative cover, and shallow snow. Generally, sites with old Douglas-fir and moderate to high canopy cover on warmer aspects and moderately steep slopes are preferred, resulting in a suite of structural stand conditions, which can be used to map mule deer winter range habitat within the interior Douglas-fir range. The increased availability of light detection and ranging (lidar) data to management agencies and the recent adoption of lidar technology by forestry agencies allow us to assess the capacity of this technology to map some variables important to winter mule deer habitat suitability, using criteria similar to those defined using conventional aerial photography. Results indicate that lidar-derived solar radiation regime, elevation, and overstorey cover are all useful attributes in decision-tree models relating lidar to conventionally derived descriptors of mule deer winter habitat. These lidar-derived models describe up to 75% of the variance in overall stand structure and confirm that this technology is a viable tool which can be used to assess habitat throughout this region.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| 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".