THz-based Density Prediction in Timber Wood using Stacked Ensemble Regression Strategy
Bibliographic record
Abstract
Density is a crucial parameter that greatly influences the mechanical properties, structural grading, and thereby, overall utilization of timber wood across diverse industrial domains. Hence, the accurate prediction of wood density is very important, which in turn enables the evaluation of defects and the estimation of moisture content in timber wood species. Although traditional density estimation techniques, such as gravimetry, X-ray densitometry, and manual inspection, are effective to some extent, they are often limited by their destructive nature, high cost, subjectivity, sensitivity, or safety concerns. To circumvent the limitations of existing techniques, terahertz (THz) technology has been explored here as a nondestructive, non-ionizing alternative for assessing the quality of timber wood. THz radiation can capture the difference in material properties arising from variations in internal structure, which in turn are correlated with wood density. In this work, a publicly available THz open dataset has been utilized, which contains the THz temporal signals of 78 samples from six diverse wood species. Here, a uniquely tailored combination of pre-processing steps, feature extraction techniques, and a stacked regression ensemble has been adopted to predict the wood density precisely. The stacked ensemble regressor sequentially integrates different ensemble learners strategically, with random forest, XGBoost, CatBoost, and the ridge regressor. This proposed approach yields a very high R2score of 0.9996, highlighting the advantages of integrating THz technology with machine learning techniques for precise, non-destructive estimation of wood density in the timber industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".