Optical characteristics of Douglas fir at various densities, grain directions and thicknesses investigated by near-infrared spatially resolved spectroscopy (NIR-SRS)
Bibliographic record
Abstract
Abstract A near-infrared spatially resolved spectroscopy (NIR-SRS) system, also called the hyperspectral imaging system, was applied in a line scanning model combined with a concentrated halogen light source (Ø 1 mm), and spatially resolved reflectance images of Douglas fir [ Pseudotsuga menziesii (Mirb.) Franco] wood were obtained. The samples with various densities, grain directions and thicknesses were observed in the NIR range of 1000–1600 nm. Then, a nonlinear curve-fitting algorithm was applied based on the steady-state diffusion theory model to estimate the absorption coefficient ( μ a ), and reduced scattering coefficients ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:msub> <m:msup> <m:mi>μ</m:mi> <m:mo>′</m:mo> </m:msup> <m:mi>s</m:mi> </m:msub> </m:mrow> </m:math> ${\mu '_s}$ ) both parallel and perpendicular to the grain direction at each measurement position. The absorption scattering coefficients at 1457 nm and two kinds of reduced scattering coefficients at 1002 nm were highly correlated with wood densities measured by an X-ray densitometer. The correlation coefficients were 0.953 and 0.987 for 3 mm and 5 mm samples, respectively, while three optical profiles were combined. It can be concluded that NIR-SRS is a fast and simple method for measuring the optical characteristics of softwood, although it has a non-homogeneous cellular structure. Sub-surface density and grain direction could be predicted with satisfactory accuracy based on a few key wavelengths without relying on multivariate statistical analysis.
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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 | 0.000 |
| 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 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".