Nondestructive measurement of microfibril angle of wood by using near-infrared spectroscopy
Bibliographic record
Abstract
Fast-growing tree species, such as Eucalyptus, are used extensively in plantations for timber, but their mechanical properties are not well understood, especially the microfibril angle (MFA), which affects wood stiffness. MFA measurement is complex and expensive, but near-infrared spectroscopy (NIRS) offers a non-destructive alternative. This study aims to evaluate the effectiveness of NIRS in predicting MFA across different regions and environments. The results showed that NIRS could predict MFA, but the accuracy varied. In Brazil, higher prediction accuracy was observed when data from multiple regions were combined. In Laos, the presence of juvenile wood significantly decreased prediction accuracy. Combining data from multiple sites improved prediction accuracy, but decreased accuracy when juvenile wood was included. The study concludes that effective MFA prediction models must consider regional and environmental differences. Creating region-specific models is necessary for reliable wood quality assessment using NIRS. This research underscores the potential of NIRS as a practical tool for wood quality evaluation, highlighting the importance of accounting for factors such as wood maturity and environmental conditions in developing robust predictive models.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".