Small Mammal Responses to Site Preparation Techniques in North Carolina Coastal Plain Pine Plantations
Bibliographic record
Abstract
Small mammals are ecologically important and should be considered as part of the sustainable management of pine (Pinus spp.) plantations, a common forest type within the southeastern United States. Few studies, however, have described how combinations of mechanical and chemical site preparation and herbaceous weed control (HWC) used in pine plantations affect pocosin small mammal communities. For 6 or 7 years after site preparation depending on treatment, we examined small mammal responses to six treatments of increasing intensity via combinations of mechanical and chemical site preparation with HWC in six loblolly pine (Pinus taeda) plantations in the Coastal Plain of North Carolina. We removal trapped for 144,000 trap-nights and captured 3,795 small mammals during winter 2002–2006 and 2008. Pine management techniques had short-term (1–2-year) effects on small mammal captures, species richness, and diversity, although timing and magnitude varied by species. Small mammal parameters were greater in strip-shear plots than in chopped plots for 2 years after site preparation, less in plots receiving chemical site preparation than in plots without chemical site preparation for 1–2 years, and greater with banded than broadcast HWC for 1 year after application. It appears that pocosin small mammal communities are sustainable within intensively managed pine stands of the southeastern coastal plain.
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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 | 0.001 | 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".