Wood density and wood shrinkage in relation to initial spacing and tree growth in black spruce (Picea mariana)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it