Radial Growth Response of Black Spruce Stands Ten Years after Experimental Shelterwoods and Seed-Tree Cuttings in Boreal Forest
Bibliographic record
Abstract
Partial cutting is thought to be an alternative to achieve sustainable management in boreal forests. However, the effects of intermediate harvest intensity (45%–80%) on growth remain unknown in black spruce (Picea mariana (Mill.) B.S.P.) stands, one of the most widely distributed boreal species with great commercial interest. In this study, we analysed the effect of three experimental shelterwood and one seed-tree treatments on tree radial growth in even-aged black spruce stands, 10 years after intervention. Our results show that radial growth response 8–10 years after cutting was 41% to 62% higher than in untreated plots, with stand structure, treatment, tree position relative to skidding trails, growth before cutting and time having significant interactions. The stand structure conditioned tree growth after cutting, being doubled in younger and denser stands. Tree spatial position had a pronounced effect on radial growth; trees at the edge of the skidding trails showed twice the increase in growth compared to interior trees. Dominant trees before cutting located close to the skidding trails manifested the highest growth response after cutting. This research suggests that the studied treatments are effective to enhance radial wood production of black spruce especially in younger stands, and that the edge effect must be considered in silvicultural management planning.
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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) | 0.001 | 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".