Hydraulic traits of Norway spruce sapwood estimated by Fourier transform near-infrared spectroscopy (FT-NIR)
Bibliographic record
Abstract
The potential of Fourier transform near-infrared (FT-NIR) spectroscopy to predict hydraulic traits in Norway spruce (Picea abies (L.) Karst.) sapwood was evaluated. Hydraulic traits tested were P50 (applied air pressure causing 50% loss of hydraulic conductivity) and RWL50 (applied air pressure causing 50% relative water loss). Samples came from 24-year-old spruce clones. FT-NIR spectra were collected from the axial (transverse) and radial surface of each solid wood sample for the prediction of P50 and RWL50. Partial least squares regression (PLS-R) models with cross validation were used to establish relationships between the FT-NIR spectra and the reference data from hydraulic properties analysis. The impact of the wavenumber range and the pretreatment during the PLS-R model development and the differences between the axial and radial surfaces were shown. Based on the values of the coefficient of determination (r2) and the root mean square error of cross validation, predicted results were evaluated as acceptable. The models from the axial surface gave better results than the models from the radial surface for P50 (r2 = 0.65), as well as for RWL50 (r2 = 0.77). The first approach to predict hydraulic properties such as P50 and RWL50 by FT-NIR spectroscopy can be regarded as successful. We conclude that the method has high potential to be put into practice as a rapid, reliable, and nondestructive method to determine P50 and RWL50.
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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 | 0.000 | 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) | 0.000 | 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".