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 (R 2 ) 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 R 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".