An innovative use of white-tailed deer (Odocoileus virginianus) foraging behaviour in impact studies
Bibliographic record
Abstract
We developed an innovative method for estimating human impacts on animal species by measuring changes in feeding behaviour. We illustrate our approach with a study of the effect of vegetation control in a power-line right-of-way (ROW) passing through essential winter habitat of white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)) at the northern limit of their range. We used giving-up densitiy (GUD; i.e., the amount of food left behind when an animal stops foraging in a patch) to evaluate, in one deer yard, if the loss of forest shelter caused by the power-line installation had a greater effect on deer than the gain of food regenerated in the cleared area. We used GUDs to compare deer estimate of habitat quality in the ROW and in the forest. Our results suggest that the ROW had a negative impact on deer. GUDs were lower in the forest compared with the ROW. Either increased metabolic costs or increased predation risk in the ROW, apparently the latter, lead deer to abandon more food in the ROW than elsewhere. Higher GUDs were strongly correlated with greater snow depth in the ROW. Deer preferred habitats at the edge of the ROW where food and cover were both available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".