Moisture and surface quality sensing of Douglas-fir (<i>Pseudotsuga menziesii</i> var. <i>menziesii</i>) veneer products
Bibliographic record
Abstract
The potential of near-infrared spectroscopy (NIRS) to estimate moisture content (MC) and surface inactivation parameters of Douglas-fir (Pseudotsuga menziesii var. menziesii) veneer products was assessed. The best prediction model for MC was produced for the lower range of MC (0%–50%) of Douglas-fir veneers. Exposure at 180°C produced surface colour changes and the CIE-L*a*b* colour parameters measuring colour changes were better estimated using the 400 nm to 900 nm spectral data than the 1100 nm to 2400 nm spectral data. Increased exposure time resulted in lower wettability and hence increasing contact angles, especially when ethylene glycol and formamide were used as solvents. NIRS-based predictions of contact angles were better when the angles were measured using formamide than when they were measured using ethylene glycol. Lap shear tensile strengths of bonds made with phenol formaldehyde (PF) resin decreased with exposure times. NIRS-based predictions of tensile strengths were also estimated and we found strong negative relationships between contact angle and tensile strength, whatever the probe solvent used (water, glycerol, ethylene glycol and formamide). It is apparent that NIRS can differentiate veneers samples that had undergone high temperature exposure, which resulted in lower wetting properties and somewhat lower adhesion bond strength.
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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".