Modelling the variability of density and bending properties of Norway spruce structural timber
Bibliographic record
Abstract
Density, modulus of elasticity (MOE), and bending strength (MOR) are important properties of structural timber, and knowledge about the variability of these properties is important to make efficient use of the timber. To utilize such information in the production of structural timber, the information must be available before sawing. This study presents models describing the variability of density and bending properties of Norway spruce (Picea abies L. Karst) boards within individual trees, as well as among trees and stands, based on geographical data and forest inventory data including external tree measurements. The models were based on 1551 boards from 17 sites in Southern Norway, Eastern Norway, and Trøndelag. Important variables describing variation in density, MOE, and MOR between sites were site index and elevation. For density, latitude gave additional information. Age, diameter at breast height, and longitudinal position within the tree were the most important variables at the tree level. The models explained major parts of the site variance of all properties, and for MOR, they explained a substantial part of the variance due to trees. In addition to being used for predicting the properties of structural timber from current forest resources, the models also provide information that can be used to predict the effects of silviculture on timber properties in future forest stands.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".