A comparative study on the performance of terahertz, near-infrared, and hyperspectral spectroscopy for wood identification
Why this work is in the frame
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Bibliographic record
Abstract
Wood species identification is of paramount significance in wood products manufacturing and applications. In contrast to traditional wood identification methods, spectroscopy-based technology offers a rapid, cost-effective, and efficient alternative. This study focuses on five wood species as experimental materials and aims to obtain three distinct wood spectra for each: near-infrared (NIR) spectra, hyperspectral image spectral information, and terahertz (THz) spectra. These spectra underwent pre-processing techniques such as Savitzky–Golay smoothing (SG), normalization, multiple scattering correction (MSC), and standard normalized variate (SNV), followed by dimensionality reduction through principal component analysis (PCA). Subsequently, the processed data were input into a partial least squares discriminant analysis (PLS-DA) for recognition. The results demonstrate the best recognition accuracy of 99.8% for THz spectra, 98.7% for NIR spectra, and 97.3% for hyperspectral image spectral information. The THz spectra exhibited the highest recognition accuracy, particularly with the SG-preprocessed THz spectra. These preprocessed spectra effectively removed noise and smoothed the spectral curves compared to the raw spectra.
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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 it