A new method for evaluating forest thinning: growth dominance in managed Pinus resinosa stands
Bibliographic record
Abstract
Growth dominance is a relatively new, simple, quantitative metric of within-stand individual tree growth patterns, and is defined as positive when larger trees in the stand display proportionally greater growth than smaller trees, and negative when smaller trees display proportionally greater growth than larger trees. We examined long-term silvicultural experiments in red pine ( Pinus resinosa Ait.) to characterize how stand age, thinning treatments (thinned from above, below, or both), and stocking levels (residual basal area) influence stand-level growth dominance through time. In stands thinned from below or from both above and below, growth dominance was not significantly different from zero at any age or stocking level. Growth dominance in stands thinned from above trended from negative at low stocking levels to positive at high stocking levels and was positive in young stands. Growth dominance in unthinned stands was positive and increased with age. These results suggest that growth dominance provides a useful tool for assessing the efficacy of thinning treatments designed to reduce competition between trees and promote high levels of productivity across a population, particularly among crop trees.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".