Rapid prediction of natural durability of larch heartwood using Fourier transform near-infrared spectroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The feasibility of Fourier transform near-infrared (FT-NIR) spectroscopy for rapidly determining the natural durability of the heartwood of larch trees (Larix decidua Mill. and Larix kaempferi (Lamb.) Carrière) was investigated. FT-NIR spectra were collected from solid wood with a fibre-optical probe. Basidiomycetes tests using Coniophora puteana and Poria placenta were carried out on larch heartwood (European standard EN 113), with pine sapwood (Pinus sylvestris L.) used as a reference. The relative resistance to decay (x value) was calculated, and durability classes were estimated according to European standard EN 350-1. Partial least squares regressions between the data sets of wood decay tests (x values) and the FT-NIR spectra were calculated. It was found that multiplicative scatter correction considerably improved the model predictability. High coefficients of correlation (r) and low root mean square errors of prediction (RMSEP) were obtained for cross validation based on wood decay tests with P. placenta (r = 0.92, RMSEP = 0.077, range 0.27-1.13) and C. puteana (r = 0.97, RMSEP = 0.078, range 0.07-1.58). Overall, NIR spectroscopy has proven to be an accurate and fast method for the nondestructive determination of natural durability, which might be highly relevant for intensive tree breeding programs and for efforts to optimize wood utilization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it