Proportional basal area method for implementing selection silviculture systems in longleaf pine forests
Bibliographic record
Abstract
Proportional basal area (Pro-B) was developed as an accurate, easy-to-use method for making uneven-aged silviculture a practical management option. Following less than 3 h of training, forest staff from a range of professional backgrounds used Pro-B in an operational-scale field study to apply single-tree selection and group selection systems in longleaf pine (Pinus palustris Mill.) stands. Field crews achieved precision levels often within 3%–5% of the 11.5 m2·ha−1 target residual basal area. By aggregating many diameter classes into only three diameter-class groups, Pro-B improves efficiency by requiring tree markers to remember only three fractions, while making a single pass through the stand. Trees of large size, specific species and with good form, broad crowns and cavities can be retained, while adjusting spacing to release residuals. Systematic quantification of marking trees for removal enables different individuals to obtain similar results. Early observations revealed encouraging levels of pine regeneration and stand development, along with continuing good volume growth rates of 3% per year. Although less certain until one or more cutting cycles are completed, these early tests indicate that a stable mature forest structure should develop, which is characterized by the presence of large trees and natural regeneration.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".