Comparison of snag densities among regeneration treatments in mixed pine–hardwood forests
Bibliographic record
Abstract
Standing dead trees (snags) are an important component of forest ecosystems, providing foraging, nesting, and roosting substrate for a variety of vertebrates. We examined the effects of four forest regeneration treatments on residual snag density and compared those with densities found in unharvested, naturally regenerated forests (controls) during the second, fourth, and sixth year after timber harvest in mixed pine–hardwood forests of Arkansas and Oklahoma. Regeneration treatments were clearcut with snag creation, shelterwood, single-tree selection, and group selection. Density of large snags (≥25.0 cm DBH) differed only during the sixth year after harvest, with shelterwoods having a lower density of large snags (1.0 snags/ha) than the control or group selection stands (4.0 and 4.2 snags/ha, respectively). Density of small snags (10.0–24.9 cm DBH) mirrored residual basal area, with controls and group-selection stands having the greatest snag densities. Creation of snags in clearcuts by injection with herbicides caused initial snag density in these areas to be greater than other treatments, but density in clearcuts declined sharply by 6 years after harvest. In the absence of snag creation, treatments such as shelterwoods that remove most trees may have snag densities below that required to address some management objectives without additional snag creation.
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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 | 0.001 | 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".