Detection of wet-pockets on the surface of Tsuga heterophylla (Raf.) Sarg. by near infrared (NIR) spectroscopy
Bibliographic record
Abstract
Abstract Multivariate statistical models capable of rapidly and accurately predicting the surface moisture content in Tsuga heterophylla were developed based on near infrared (NIR) spectra of small specimens precisely conditioned within the hygroscopic range. In an initial research phase, the applicability of NIR as predictor of surface moisture was investigated. The first derivative spectra in the range of 1300–2100 nm yielded the best results. The partial least squares regression (PLS-1) model had coefficient of determination (R2) of 0.98, root mean square error of cross validation (RMSECV) of 0.97%, root mean square error of prediction (RMSEP) of 1.05%, and ratio of performance to deviation (RPD) of 7.25. In a subsequent phase, an inline pilot-plant NIR system combined with this PLS-1 model was constructed. The prediction ability of the NIR system was tested with line speeds of 0, 100, 200, and 400 mm s-1 on kiln-dried full-length lamination boards classified as “wets” after conventional kiln drying. In a calibrated range of moisture content (0–25.4%), the NIR system demonstrated R2 values of 0.79 and 0.74, RMSEP values of 3.13 and 3.28, and RPD values of 2.18 and 1.67 at a line speed of 0 and 100 mm s-1, respectively, regardless of the presence of knots and surface roughness. These results demonstrate that the NIR system at a line speed of 0–100 mm s-1 could be used to provide entire surface moisture distribution and to detect local moisture peaks that indicate surface wet-pockets in kiln-dried lumber destined for lamination.
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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 | 0.000 |
| 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".