Effects of partial cutting in winter on white-tailed deer
Bibliographic record
Abstract
We documented how commercial logging influenced the spatial behavior and nutritional ecology of northern white-tailed deer (Odocoileus virginianus). Using periodic browse surveys, we estimated the additional biomass of twigs available from felled trees to deer in the Pohénégamook wintering area (25 km2) at 55 kg/ha in a 43-ha cut conducted in 1995-1996 that aimed to favor conifers by removing overtopping deciduous trees. Over the entire winter, deer used 54% of the browse made available by the felling residues. The use of the cutover, estimated by pellet group census, was five times greater than the average recorded over the entire wintering area. Felled trees provided approximately 35% of the food intake of the animals that have used the cutover. Of 30 deer fitted with radio collars, the cutover attracted only those whose range neighbored the logging area (<2 km). In preference tests carried out in the winter of 1996-1997, deer showed no preference for twigs from newly cut trees over those from trees cut earlier in the winter, nor for twigs from treetops (browse made accessible during the logging operation) over twigs from saplings (browse usually accessible in winter). If commercial logging is conducted in winter as a means of providing emergency food during snowy winters to enhance deer survival, our results suggest that partial cutting may be ineffective because felling residues were used only by deer found near the cutover and because of the difficulties of logging in deep snow.
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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 | 0.000 | 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) | 0.000 | 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".