Determination of radial profiles of wood properties using a near infrared scanning system
Bibliographic record
Abstract
A near infrared (NIR) spectrometer, equipped with a 2-mm diameter fiber optic probe, was used to develop a rapid and automated method for determining the radial profiles of wood properties for six tree species of the boreal forest of eastern Canada. Partial least squares regressions were performed using individual spectra, collected every 5 mm, from the pith to the bark, using 1,538 samples previously scanned by SilviScan to provide reference data. Results obtained for density (r 2 of 0.57 to 0.65; SE of 21 to 33 kg/m 3 ), microfibril angle (0.56 to 0.82, 2.3 to 4.8°) and modulus of elasticity (0.71 to 0.85, 1.3 to 1.8 GPa) were suitable for the purpose of this study. This method was then applied to more than 30,000 tree cores and the resulting radial profiles of the properties were consistent with literature. Area-weighted average predictions also gave reliable estimates at the disk scale. NIR-estimated values were strongly correlated to the measured density (r of 0.83 to 0.93) and modulus of elasticity (0.82 to 0.88) and moderately correlated to measured microfibril angle (0.64 to 0.78). Radial profiles of wood properties are needed to characterize the proportions of juvenile and mature wood. The developed method therefore offers interesting opportunities for estimating the quality of wood fiber in trees growing in plantation or natural forest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".