Effects of commercial thinning on characteristics of naturally regenerated coniferous stands from Eastern North-America
Bibliographic record
Abstract
Commercial thinning is a silvicultural treatment that has been practiced for centuries in Europe. However, in Eastern Canada, its application to naturally regenerated stands is much more recent, and long-term monitoring of this treatment realized in an operational context is rare. We monitored 135 paired sample plots (thinned and control) over a 20-year period. The plots are in stands dominated by either black spruce ( Picea mariana (Mill.) B.S.P.), jack pine ( Pinus banksiana Lamb.), or balsam fir ( Abies balsamea (L.) Mill.) and distributed throughout the boreal and temperate forests of Québec (Canada). Twenty years after treatment, thinning increased quadratic mean diameter (QMD) for balsam fir (1.7 cm) and jack pine (0.7 cm), while for black spruce the change in QMD varied according to the QMD before treatment. Periodic annual increment in gross merchantable volume of thinned and control plots was similar for balsam fir and jack pine but was less in thinned black spruce plots during the first 5 years. Thinning did not affect mortality, which remained low until 15 years after treatment. As commercial thinning should gain popularity over the next years, our study provides a benchmark of the expected effects when the treatment is performed in an operational context.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".