Modeling flexural properties in white spruce (<i>Picea glauca</i>) and jack pine (<i>Pinus banksiana</i>) plantation trees
Bibliographic record
Abstract
Mixed models combining random coefficient effect and covariance patterns were used to investigate mechanical property variations in jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) trees. Modulus of elasticity (MOE) and modulus of rupture (MOR) were measured by conducting three-point bending tests on small defect-free samples selected from different radial positions and at a height of 2.5 m above ground within the stems. The objective of the paper was to build statistical predictive models describing the radial variations in stems for wood mechanical properties using easily measurable explanatory variables that are typically available in the wood manufacturing industry: distance from pith, tree height and diameter, and spacing. The explanatory variables integrated into the models explained MOE adequately, whereas MOR appeared harder to predict with only these variables and at this resolution. For white spruce, the best mixed-effects models explained 80% and 61% of the variation in MOE and MOR, respectively. For jack pine, it was 51% and 33% for the same response variables. These results are a step toward models that could be used in sawing simulation software designed to estimate the internal properties of sawlogs and, as a result, better predict lumber and pulp chip quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".