Impact of precommercial thinning on tree growth, lumber recovery and lumber quality in <i>Abies balsamea</i>
Bibliographic record
Abstract
Precommercial thinning (PCT) is often used to improve stand growth and value. While PCT may accelerate tree growth and reduce mortality, it may also have a negative effect on product quality. This study examined the effect of moderate and heavy thinning on tree growth, lumber recovery and quality in a natural balsam fir [Abies balsamea (L.) Mill.] PCT trial 35 years after thinning. Compared with the control, the heavy thinning increased merchantable tree diameter, stem volume per tree and lumber volume recovery per tree by 41.1%, 100.9% and 92.7%, respectively, reduced the Select Structural grade (the best grade) recovery by 33.7%. Thinning did not affect the no. 2 and better grade yield. There was a 12.2% and 15.0% difference, respectively, in the lumber bending modulus of elasticity (MOE) and modulus of rupture (MOR) between the control and heavy thinning. Moderate thinning had little impact on the visual grade recovery, lumber bending MOE and MOR. Heavy thinning is recommended if the goal is to get sizeable sawlogs in the shortest time, whereas moderate thinning is preferable if the intention is to minimize the negative effects on lumber quality while retaining modest tree growth and lumber recovery. Overall, PCT of very dense young balsam fir stands appears to be an effective and viable silvicultural treatment.
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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 | 0.001 | 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 | 0.001 |
| 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".