Use of multiobjective optimization models to examine behavioural trade-offs of white-tailed deer habitat use in forest harvesting experiments
Bibliographic record
Abstract
I evaluated the habitat use of white-tailed deer (Odocoileus virginianus Zimmermann) in a boreal mixedwood forest managed for timber production in northern Saskatchewan, Canada, to determine which kinds of factors (e.g., forage abundance, canopy cover) influenced habitat use, if white-tailed deer considered these habitat factors in isolation or instead there was evidence of trade-offs, and whether the identification of the factors or the way in which they were traded off varied subsequent to the introduction of harvested patches or changes in hunting traffic. Habitat use was estimated through ground-based surveys before and after the elimination of hunting traffic in a partially harvested site and before and after harvesting in a formerly hunted but unharvested site. Forage abundance was always a significant, or marginally significant, predictor of habitat use. Canopy cover was only important in sites with limited water access. In the presence of hunting traffic, white-tailed deer balanced multiple habitat factors (e.g., food access vs. road avoidance); when hunting traffic was eliminated, the trade-off behaviour was abandoned. The variety in behaviour that the white-tailed deer exhibited in my study implies that to successfully protect habitat, it may not be sufficient to simply identify the factors that influence habitat use (and presumably value). We may also need to understand how the animals deal with these factors under different conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".