Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of “group method of data handling” (GMDH) neural network
Bibliographic record
Abstract
Abstract The effect of thermal modification (TM) on the color of western hemlock wood and its physical and mechanical properties were investigated. The focus of this study was the prediction of material properties of thermally modified wood based on the color change via the “group method of data handling (GMDH)” neural network (NN). The NN was trained by color parameters for predicting the equilibrium moisture content (EMC), density, porosity, water absorption (WA), swelling coefficient, dynamic modulus of elasticity (MOEdyn) and hardness. The color parameters showed a significant correlation with temperature and are well correlated with the heat treatment (HT) intensity. Color parameters combined with the GMDH-type NN successfully predicted the physical properties of the material. The best correlation was achieved with the swelling coefficient, EMC and WA. All these properties were significantly influenced by HT. The color parameters did not seem suitable for predicting the wood hardness and MOEdyn. The GMDH NN shows a higher model accuracy than the multivariate linear and partial least squares (PLS) regression models.
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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 | 0.000 | 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".