Influence of Stem Taper on the Yield of Black Spruce Stands and Trees Following Commercial Thinning
Bibliographic record
Abstract
In this work, we examine the effects of commercial thinning on stand volume and individual stem form in nine naturally regenerated black spruce (Picea mariana (Mill.) B.S.P.) stands. We compared these study sites with controls in the commercial boreal forest of northern Quebec, Canada. At stand level, dendrochronological data provided insight into changes in stand volume ten years after thinning. Analysis of a subsample of six individual trees from each commercially thinned stand and three individual trees from the controls illustrated the role of thinning on stem shape development. Although average volume increased for residual stems in thinned stands slightly more than in the controls (110% versus 106%), the treatment effect stand-level volume increment or stand-level total volume harvested (ten years after treatment) was not statistically significant. Moreover, at tree level, thinning did not significantly affect stem volume increment. However, radial growth increment significantly increased after treatment. Trees from commercially thinned stands showed a significantly higher growth increment along the lower first two-thirds of the stem. Response to thinning at tree level correlated strongly with the size and number of harvested competitors around a residual stem. We conclude that commercial thinning modified wood allocation rather than wood volume and did not affect taper and stem shape. These patterns of post-cutting growth are explained by wood allocation following thinning. After commercial thinning, growth increment is favored at the expense of height growth. As the treatment effect was found at the stem scale rather than at the stand scale, further research is needed in regard to commercial thinning treatments to investigate how to increase productivity at the stand scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".