Estimation of wood stiffness of increment cores by near-infrared spectroscopy
Bibliographic record
Abstract
The use of calibrated near-infrared (NIR) spectroscopy for predicting the radial variation of the longitudinal modulus of elasticity (EL) of increment cores is described. Sets of Eucalyptus delegatensis R.T. Baker (alpine ash) and Pinus radiata D. Don (radiata pine) samples were characterized in terms of EL(SS) (estimated stiffness based on a combination of SilviScan-2 diffractometric data and measured density (R. Evans and J. Ilic. 2001. For. Prod. J. 51(3): 5357)). NIR spectra, obtained from the radiallongitudinal face of each sample, were used to develop EL(SS) calibrations for the E. delegatensis and P. radiata sample sets and the two sets combined. The relationships between laboratory-determined EL(SS) and NIR-fitted EL(SS) were good in all cases. EL(SS) was estimated in separate test sets and found to correlate well with measured EL. NIR spectra were obtained in 15-mm sections from the radiallongitudinal face of two intact P. radiata increment cores. EL(SS) of each section was estimated using the P. radiata and the combined P. radiata and E. delegatensis calibrations. NIR estimates of EL(SS) were in good agreement with SilviScan-2 determined stiffness indicating that NIR spectroscopy can be successfully used to estimate radial variation in wood stiffness of increment cores.
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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) | 0.000 | 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) | 0.001 | 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".