Wood density and wood shrinkage in relation to initial spacing and tree growth in black spruce (Picea mariana)
Bibliographic record
Abstract
Abstract This study has quantified basic wood density and various types of wood shrinkage in relation to initial spacing (or initial planting density) and tree growth based on a 48-year-old black spruce (Picea mariana) spacing trial in eastern Canada. A total of 139 sample trees were collected from four initial spacings (3086, 2500, 2066, 1372 trees/ha) for this study. Analyses of variance (ANOVA) show that initial spacing is the most important parameter affecting wood density significantly, followed by tree diameter at breast height (DBH) class. With increasing spacing, wood density, radial and volumetric shrinkage tend to decrease, whereas longitudinal shrinkage tends to increase gradually. The largest spacing has the lowest wood density, the smallest transverse shrinkage and the largest longitudinal shrinkage. Path analysis indicates that wood density is the most important parameter affecting transverse shrinkage, followed by the distance from the pith. Furthermore, much of the variation of the transverse shrinkage with wood density may be due to the initial spacing and tree DBH class. Path analysis also reveals that longitudinal shrinkage is mainly related to log height and tree DBH class. With increasing log height, longitudinal shrinkage tends to increase, and transverse shrinkage tends to decrease. With increasing DBH class, the trees tend to have an increasing longitudinal shrinkage and a decreasing transverse shrinkage. Overall, this study suggests that a large increase in the initial spacing (e.g., 1372 trees/ha) might lead to a significant reduction in both wood density and transverse shrinkage, and a significant increase in longitudinal shrinkage in black spruce.
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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) | 0.001 | 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".