Modelling of pH effects and CIE L*a*b*colour spaces of beech wood-inhabiting fungi by NIRS
Bibliographic record
Abstract
A combination of statistical techniques of analyses were used to evaluate the potential of International Commission on Illumination (CIE) lightness (L*), redness (a*) and yellowness (b*) colour space system and near-infrared spectroscopy (NIRS) to assess surface changes in relation with progressive decay of beech (Fagus grandifolia Ehrh.) by wood-inhabiting lignicolous fungi Inonotus hispidus, Trametes versicolor and Xylaria polymorpha. pH effects based modelling predictions of beech earlywood and latewood tissues were also included. Multivariate analysis techniques included response surface optimization, sample-specific standard error of prediction (SEP) method and projection to latent structures partial least squares (PLS) regression. Strong statistical relationships were derived for pH predictions with R2 values ranged: from 0.77 to 0.84 for I. hispidus; from 0.77 to 0.84 for T. versicolor and from 0.83 to 0.91 for X. polymorpha. R2 values for CIE-based L*a*b* colour space measurements ranged: from 0.43 to 0.69 (L*), 0.66 to 0.76 (a*), 0.42 to 0.53 (b*) for I. hispidus; from 0.59 to 0.69 (L*), 0.69 to 0.79 (a*), 0.64 to 0.79 (b*) for T. versicolor; and from 0.51 to 0.75 (L*), 0.89 to 0.94 (a*), 0.85 to 0.89 (b*) for X. polymorpha. Multivariate technical analysis (response surface analysis, sample-specific SEP, PLS regression) of CIE L*a*b* system and NIRS results should be able to characterize pH effects and surface changes of wood spalted by lignicolous fungi as a quick and reliable non-destructive method relevant to wood-spalting concerns and the forest products industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".