Main mechanical properties of new Chinese fir clones and their rapid prediction by near-infrared spectroscopy
Bibliographic record
Abstract
Owing to its rapidity and accuracy, near-infrared (NIR) spectroscopy is a powerful tool to establish appropriate prediction models with an innovative method to evaluate wood properties. To reveal the mechanical qualities of clonal Chinese fir woods and to determine sound prediction models of mechanical properties, four main mechanical properties of six Chinese fir clones (Yang 020, Yang 061, Kaihua 3, Kaihua 13, Daba 8, Kailin 24) were evaluated by NIR spectroscopy. The clones Kaihua 13, Kailin 24, and Yang 020 showed good mechanical properties. To estimate mechanical properties with NIR spectroscopy, different methods should be adopted for different properties. The average spectra of radial and tangential sections combined with multiple scattering correction (MSC) and Savitzky–Golay (S–G) smoothing methods were used to predict the modulus of rupture and modulus of elasticity. By adopting cross section spectra and taking MSC and S–G smoothing methods for pretreatment, the models of compressive strength parallel to grain could deliver the best results. For wood hardness, the models established with average spectra of three sections and first-derivative method were preferred. The correlation coefficients of the prediction models were between 0.84 and 0.90, and those of the calibration models were between 0.75 and 0.96.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".