Precommercial thinning increased diameter growth while maintaining mixedwood stands composition, 15 years after treatment
Bibliographic record
Abstract
Precommercial thinning could be a valuable tool for climate change adaptation, as it can promote stand diversity while increasing productivity. Softwood and hardwood stands are usually thinned following different methods, and we lack recommendations for application in mixedwood stands. We evaluated the effects of precommercial thinning on the growth and composition of balsam fir ( Abies balsamea L.)–birch stands dominated by paper and yellow birch ( Betula papyrifera Marsh. and Betula alleghaniensis Britt.), comparing methods and production objectives (systematic release-softwood, systematic release-mixedwood, crop-tree release-mixedwood, and crop-tree release-hardwood) in Québec (Canada). Precommercial thinning increased tree-level and stand-level growth, especially for both birches. Compared to the non-thinned control, thinning increased tree diameter at breast height annual increments by 60%–107%, with similar results among modalities. Thinning changed initial stand composition, but mixedwood production plots composition was similar to that of the control 15 years after treatment. Promoting diversity is often perceived to be made at the expense of wood production. Our results suggest that we can obtain both increased growth and maintain diversity. Precommercial thinning is currently suggested as an adaptation tool that can foster drought resistance. Based on our study, we suggest it could serve another purpose in climate-adapted forest management, that is, maintaining diversity and thus increasing resilience to disturbances.
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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) | 0.001 | 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) | 0.003 | 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".