Commercial Thinning to Meet Wood Production Objectives and Develop Structural Heterogeneity: A Case Study in the Spruce-Fir Forest, Quebec, Canada
Bibliographic record
Abstract
We evaluated the effectiveness of commercial thinning mainly from below (CT; 0, 26%, 32% and 40% merchantable basal area removals) in meeting wood production demands and developing structural heterogeneity in a balsam fir (Abies balsamea (L.) Mill) and spruce (Picea spp.) stand. After 10 years, 32%–40% removals showed a 12%–18% increase in mean diameter and 27%–38% increase in gross merchantable volume (GMV) per tree compared to the unthinned control. At the stand level, all thinning treatments generated as much cumulative GMV (harvested volume + GMV after 10 years) and gross sawlog volume per hectare as the unthinned control. As for stand structure, eight out of nine thinned experimental units showed increased structural heterogeneity after 10 years, i.e., irregular, positively-skewed diameter distribution with an elongated right tail toward larger trees. The diameter distribution in the unthinned control became more symmetric, unimodal and regular over time, with fewer saplings than at the beginning of the experiment and lower density of larger trees compared to CT. Regeneration density and stocking were abundant in all treatments, largely dominated by balsam fir. Results indicate that thinning can be used to meet wood production objectives and help develop structural heterogeneity in this forest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".