Impact of Spacing on Juvenile Wood and Mature Wood Properties of White Spruce (Picea glauca)
Bibliographic record
Abstract
The impact of spacing on the growth rate, relative density, and tracheid length of juvenile and mature wood was evaluated. Ten plantation-grown white spruce (Picea glauca Moench. Voss) trees 38 years old grown at each of 3 spacings, i.e., 1.8×1.8 m, 2.7×2.7 m, and 3.6×3.6 m at Stanley, 30km west of Thunder Bay, Ontario were randomly selected for this study. Samples consisting of 12-mm-diameter increment cores were extracted at breast height from the south aspect of each tree. A contrast test was applied statistically to test the difference between spacings. Boundaries of juvenile and mature wood were demarcated with an earlywood tracheid length variation method. The juvenile-mature transition period of P. glauca ranged from 8 to 20 years. For all 3 plantation spacings, the growth rate in juvenile wood was greater than that of mature wood. In contrast, the mean earlywood tracheid length in juvenile wood, ranged from 1.8 to 2.0 mm which is shorter than those of mature wood which ranged from 2.9 to 3.0 mm. The relative densities (acetone extractive free) of juvenile and mature wood were found to be very similar for each spacing. The highest mean relative densities which occurred at the narrowest spacing were 0.38 to 0.37 for juvenile and mature wood, respectively. The mean relative densities of trees at the 2 wider spacings ranged from 0.33 to 0.35. Results of the contrast test indicated that there was no statistically significant impact of spacing on tracheid length, except in juvenile wood at the widest spacing which has long tracheids. However, arithmetically, a trend of increasing tracheid length with increasing spacing was discerned. This observation does not agree with most in the reports literature. Further studies are needed to clarify these contradictory results.
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.
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 | 0.000 | 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".