Estimation of the physical wood properties of green <i>Pinus taeda</i> radial samples by near infrared spectroscopy
Bibliographic record
Abstract
The application of near infrared (NIR) spectroscopy to the green wood of radial samples (simulated increment cores) and the development of calibrations for the prediction of wood properties are described. Twenty Pinus taeda L. (loblolly pine) radial strips were characterized in terms of air-dry density, microfibril angle (MFA), and stiffness. NIR spectra were obtained in 10-mm steps from the radial longitudinal and transverse faces of each sample and used to develop calibrations for each property. NIR spectra were collected when the wood was green (moisture content ranged from approximately 100% to 154%) and dried to approximately 7% moisture content. Relationships between measured and NIR estimates for green wood were good; coefficients of determination (R2) ranged from 0.79 (MFA) to 0.85 (air-dry density). Differences between calibrations developed using the radial longitudinal and transverse faces were small. Calibrations were tested on an independent set. Predictive errors were relatively large for some green samples and relationships were moderate; R2p ranged from 0.67 (MFA) to 0.81 (stiffness). Dry wood calibrations demonstrated strong predictive relationships with R2p ranging from 0.87 (air-dry density) to 0.95 (stiffness). NIR spectroscopy has the potential to predict the air-dry density, MFA, and stiffness of 10-mm sections of green P. taeda wood samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".