Application of Near-Infrared Spectroscopy to Determine the Juvenile–Mature Wood Transition in Black Spruce
Bibliographic record
Abstract
Abstract The potential of near-infrared spectroscopy (NIRS) to determine the transition from juvenile to mature wood in black spruce (Picea mariana (Mill.) B.S.P.) was assessed. In total, 127 wood samples were harvested from 50 sites located across the black spruce–moss domain in the province of Québec, Canada. Mechanical wood properties were determined by SilviScan. NIR spectra were collected on the transverse face of the samples. Good to excellent calibration statistics (R2, ratio of performance to deviation) were obtained for basic density (0.85, 1.8), microfibril angle (0.79, 2.2), and modulus of elasticity (0.88, 2.9). Two-segment linear regressions were applied to microfibril angle profiles to determine the transition age and then calculate the juvenile and mature wood properties. The values obtained using SilviScan data were compared with those obtained using NIRS predicted data. Using SilviScan data, the average transition age was 23 years, with a standard deviation of 7 years. The correlation was moderate for the transition age (r = 0.592, P < 0.0001), which was slightly underestimated by NIRS with a mean prediction error (and 95% limits of agreement) of −2.2 ± 6.3 years (−14.6/10.1). These results suggest that the transition age from juvenile to mature wood could be predicted by NIRS. This article makes some recommendations to improve method accuracy for operational use.
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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) | 0.000 | 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 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".