Effect of Tree Spacing on Tree Level Volume Growth, Morphology, and Wood Properties in a 25-Year-Old Pinus banksiana Plantation in the Boreal Forest of Quebec
Bibliographic record
Abstract
The number of planted trees per hectare influences individual volume growth, which in turn can affect wood properties. The objective of this study was to assess the effect of six different plantation spacings of jack pine (Pinus banksiana Lamb.) 25 years following planting on tree growth, morphology, and wood properties. Stem analyses were performed to calculate annual and cumulative diameter, height, and volume growth. For morphological and wood property measurements several parameters were analyzed: diameter of the largest branch, live crown ratio, wood density, and the moduli of elasticity and rupture on small clear samples. The highest volume growth for individual trees was obtained in the 1111 trees/ha plantation, while the lowest was in the 4444 trees/ha plantation. Wood density and the moduli of elasticity and rupture did not change significantly between the six plantation spacings, but the largest branch diameter was significantly higher in the 1111 trees/ha (3.26 cm mean diameter) compared with the 4444 trees/ha spacing (2.03 cm mean diameter). Based on this study, a wide range of spacing induced little negative effect on the measured wood properties, except for the size of knots. Increasing the initial spacing of jack pine plantations appears to be a good choice if producing large, fast-growing stems is the primary goal, but lumber mechanical and visual properties could be decreased due to the larger branch diameter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".