Nondestructive estimation of <i>Pinus taeda</i> L. wood properties for samples from a wide range of sites in Georgia
Why this work is in the frame
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Bibliographic record
Abstract
Preliminary studies based on small sample sets show that near infrared (NIR) spectroscopy has the potential for rapidly estimating many important wood properties. However, if NIR is to be used operationally, then calibrations using several hundred samples from a wide variety of growing conditions need to be developed and their performance tested on samples from new populations. In this study, 120 Pinus taeda L. (loblolly pine) radial strips (cut from increment cores) representing 15 different sites from three physiographic regions in Georgia (USA) were characterized in terms of air-dry density, microfibril angle (MFA), and stiffness. NIR spectra were collected in 10-mm increments from the radial longitudinal surface of each strip and split into calibration (nine sites, 729 spectra) and prediction sets (six sites, 225 spectra). Calibrations were developed using untreated and mathematically treated (first and second derivative and multiplicative scatter correction) spectra. Strong correlations were obtained for all properties, the strongest R 2 values being 0.83 (density), 0.90 (MFA), and 0.93 (stiffness). When applied to the test set, good relationships were obtained (R p 2 ranged from 0.80 to 0.90), but the accuracy of predictions varied depending on math treatment. The addition of a small number of cores from the prediction set (one core per new site) to the calibration set improved the accuracy of predictions and importantly minimized the differences obtained with the various math treatments. These results suggest that density, MFA, and stiffness can be estimated by NIR with sufficient accuracy to be used in operational settings.
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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 | 0.001 | 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) | 0.000 | 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