Determination of Optical Parameters and Moisture Content of Wood with Visible–Near Infrared Spectroscopy
Bibliographic record
Abstract
We used the Kubelka-Munk theory equations for calculating the absorption coefficient (K λ ), the scattering coefficient ( S λ ), the transport absorption (σ λa ), the reduced scattering coefficient [σ λs (1 – g)] and the penetration depth (δ λ ) from visible-near infrared reflectance spectra acquired over thin samples of quaking aspen and black spruce conditioned at three different moisture levels. The computed absorption and scattering coefficients varied from 0.1 mm −1 to 4.0 mm −1 and from 5.5 mm −1 to 10.0 mm −1 , respectively. The absorption coefficients varied according to the absorption band, but the scattering coefficients decreased slowly towards high wavelengths. The sample moisture content was then estimated using the partial least squares (PLS) regression method from the K λ and/or S λ spectra, and the resulting PLS models were compared to those obtained with raw and transformed [multiplicative scatter corrected (MSC), first and second derivative] absorption spectra. The best PLS models for black spruce, quaking aspen and both species were obtained when only the 800–1800 nm range was used with the raw or MSC spectra. They led to a root mean square error of cross validation ( RMSECV) of 1.40%, 1.09% and 1.23%, respectively, and to a coefficient of determination ( R 2 CV ) higher than 0.94. We also found that the K λ spectra between 800 nm and 1800 nm can provide PLS models having an acceptable accuracy for moisture content estimation ( R 2 CV = 0.83 and RMSECV = 2.32%), regardless of the species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".