Quantifying the influence of live crown ratio on the mechanical properties of clear wood
Bibliographic record
Abstract
Conceptual models of wood formation suggest that trees with large crowns produce wood with reduced mechanical properties due to enhanced auxin production, but few studies have explicitly examined the relationship between crown dimensions and wood properties. Using white spruce (Picea glauca (Moench) Voss) trees harvested from spacing and thinning trials in central Ontario, Canada, this study examines how live crown ratio influences the strength and stiffness of wood. Modulus of rupture (MOR) and modulus of elasticity (MOE) were measured by conducting three-point bending tests on small (150 × 10 × 10 mm) defect-free samples selected from different radial positions at three heights within the stems. MOR and MOE were strongly and positively related to cambial age, and also increased slightly with sampling height. In addition, MOR showed a significant decrease with increasing live crown ratio – calculated as the ratio of crown length to tree height – in both the spacing and the thinning trials. However, MOE decreased significantly with live crown ratio only in the spacing trial, where the younger trees had a larger range of crown ratios. These results provide tentative support for models of wood formation that link wood quality with crown development, suggesting that crown metrics could be used to predict wood properties before harvest, but doing so may be problematic in mature stands that exhibit less variability in crown dimensions.
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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 | 0.001 | 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 | 0.001 | 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".