Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.)
Bibliographic record
Abstract
Abstract The stiffness and strength, modulus of elasticity (MOE) and modulus of rupture (MOR), as well as density, moisture content, microfibril angle and diffraction pattern coefficient of variation of azimuthal intensity profile (ICV) was determined for 259 small clear specimens. These samples represent 38 old- and second-growth western hemlock (Tsuga heterophylla) trees harvested from several sites in coastal British Columbia, Canada. The data were analyzed by classic statistical regression techniques to reveal interrelations among the mechanical properties and the inherent wood properties. Simultaneously, the predictive power of artificial neural networks was evaluated with the same data set by employing several optimization techniques. Regression analysis of wood density and the flexural strength properties resulted in R2 of 0.172 and 0.332 for MOE and MOR, respectively. The most efficient network model proved to be far superior demonstrating correlation coefficients with models for MOE ranging between 0.693 and 0.750, and the corresponding MOR models ranging between 0.438 and 0.561 in all testing phases. It is apparent that neural networks have the potential and capacity to self-train and become powerful adaptive systems that can predict the strength and stiffness of wood samples. The neural network analysis also revealed the importance level of each independent variable on both MOE and MOR properties.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".