Prediction of<i>Pinus sylvestris</i>clear-wood properties using NIR spectroscopy and biorthogonal partial least squares regression
Bibliographic record
Abstract
Thirteen wood parameters were predicted using near infrared (NIR) spectra in the range 780–2380 nm modelled by biorthogonal partial least squares regression. The analysis of parameters and NIR measurements was done on clear-wood samples from the base and midstem of Scots pine ( Pinus sylvestris L.) from trees at two sites. Calibrations based on the measured parameters at seven growth rings (cambial age ranging between 6 and 42 years) could be divided into three groups: (i) the best accuracy was found for longitudinal modulus of elasticity (r > 0.9) followed by bending, compression, and cell length (0.8 < r < 0.9); (ii) microfibril angle, longitudinal hardness, proportion of latewood, and creep with correlations in the range of 0.7–0.8; and (iii) tangential hardness, cell diameter, and cell wall thickness with 0.4 < r < 0.7. It was also shown that juvenile (cambial age ≤20 years) and mature wood can be classified using NIR techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".