Ring width distribution as a valuable anatomical trait for predicting bending strength and rigidity in European oak wood beams
Bibliographic record
Abstract
Ring width variability affects the elasticity and strength of wood members. This study was conducted to determine which dispersion statistics of annual ring width distributions measured in cross section in European oak ( Quercus robur L.) wood beams are useful as covariates in models for predicting modulus of elasticity ( MOE) and modulus of rupture ( MOR). For this purpose, 21 European oak trees growing in north-western Spain were felled, logged, and sawn. The planks obtained were air-dried and surfaced into beams (50 mm × 100 mm × 2000 mm), which were visually graded according to the UNE 56544:2022 standard. MOE, MOR, moisture content and density (determined according to EN 408:2010 standard) and abundance of sapwood and ring widths were determined in 30 beams apt for structural purposes. MOE was significantly related to standard deviation, variance and interquartile range of the ring width distribution in the beam. MOE was also related to mean ring width ( r = –0.52, p < 0.01) and maximum ring width per beam ( r = –0.54, p < 0.01), an easy to measure variable. MOR was also related to ring width distribution parameters, although yielding lower r values. The influence of ring width evenness and maximum ring width can be considered to improve visual strength grading standards for European oak timber.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".