Short-term effects of loblolly pine thinning intensity on coverage of preferred white-tailed deer forage plants
Bibliographic record
Abstract
Commercial thinning and prescribed fire can improve habitat quality for white-tailed deer (Odocoileus virginianus) in loblolly pine (Pinus taeda L.) stands by increasing coverage of forage plants. However, the relationships among thinning intensity, prescribed fire, and deer forage have not been quantified. We estimated percent cover of deer forage plants in five loblolly pine stands thinned to basal areas of 11 m2·ha–1 (low), 14 m2·ha–1 (medium), and 18 (high) m2·ha–1 in 2017 in Georgia, USA. We applied prescribed fire in 2018. From years 1 to 2 post-treatment, cover of total deer forage increased 26% and 29% in the low and medium basal area treatments, respectively, compared with 19% in the high basal area treatment. Similarly, the increase in forb coverage was greater for the medium (13%) and low (11%) basal area treatments than for the high (6%) basal area treatment. Increases in vine and bramble coverage were greater in unburned medium basal area units. Woody browse was not affected by any treatment. Our results suggest that thinning loblolly pine stands to 14 m2·ha–1 can increase coverage of deer forage plants during the first two growing seasons post-thinning, but deer forage was not greater in stands thinned to <14 m2·ha–1 2 years post-thinning.
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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".