Contrasting the summer ecology of white-tailed deer inhabiting a forested and an agricultural landscape
Bibliographic record
Abstract
: We compared habitat use, home range size, movements, and activity during summer between rural (12 animals km-2) and forest (<; 1 animal km-2) white-tailed deer populations, hypothesizing that competition for natural forage at high density would influence deer behaviour. Biomass of preferred forage at forester sites was 6 times greater in the forest than in the rural landscape. Forest deer avoided conifer and mixed stands, whereas rural deer tended to avoid stands of shade-tolerant hardwoods. Rural deer intensified their use of cultivated fields at night and ate a greater variety of native plants than forest conspecifics, including species rarely consumed by forest deer (e.g., ferns). Rural deer used smaller home ranges but moved at a greater rate than forest counterparts. Activity pattern of deer did not differ between the two landscapes, with peaks at dawn and dusk. Our results suggest that rural deer adapted to the rarity of natural forage by exploiting agricultural crops.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".