Winter habitat selection by white-tailed deer on Anticosti Island 2: relationship between deer density from an aerial survey and the proportion of balsam fir forest on vegetation maps
Bibliographic record
Abstract
Determining at what scale to operate and how much cover is needed are important questions for winter habitat management of white-tailed deer, Odocoileus virginianus (Zimmermann, 1780), through logging. We used binary cover maps (reclassified forest vegetation maps) and windows of different sizes (0.2 km × 0.2 km, 0.5 km × 0.5 km, 1 km × 1 km, 2 km × 2 km, and 3 km × 3 km) to describe the relationship between deer density from an aerial survey and the proportion of balsam fir, Abies balsamea (L.) P. Mill., forest (BF) cover in a 270-km2 block on Anticosti Island, Quebec. Maximum white-tailed deer densities reached were quite similar (31–34 deer/km2) irrespective of window size, except for the 3 km × 3 km window for which maximum density remained half lower. Density increased with the amount of BF cover and then reached a plateau above 60% or 70% (two smaller windows) or decreased above 50% or 60% (1 km × 1 km and 2 km × 2 km windows). Results confirm goals previously used for habitat management of deeryards. This new method allows greater flexibility in research applications for describing density–cover relationships because both scale and proportion of cover can be analysed simultaneously.
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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 | 0.001 |
| 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) | 0.002 | 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".