Veneer surface roughness and compressibility pertaining to plywood/LVL manufacturing. Part I. Experimentation and implication
Bibliographic record
Abstract
Extensive experiments were conducted to examine the transverse compression behavior of trembling aspen (Populus tremuloides) veneer at ambient and controlled temperature and moisture content (MC) environments, and the relationship between contact area, veneer surface roughness, and applied load. Based on the results, a novel method was developed to characterize surface roughness/quality of wood veneer in terms of its compression behavior. This method may have significant implication on both theory and practice. In theory, the general wood transverse compression theory needs to be revised to include four stages instead of the commonly defined three. The first stage, which has long been overlooked but is critically important, could be named "progressive contact." During this stage, the contact area increases nonlinearly with the load applied. It is this stage that reveals the interfacial contact of veneer-to-veneer or veneer-to-plate and the minimum veneer compression required for achieving adequate contact. With the inclusion of the first stage, the yield displacement also needs to be redefined. In practice, the method provides a fast and objective way of evaluating veneer surface roughness/quality for plywood/LVL manufacturing. Furthermore, the minimum compression required and yield displacement of wood veneer derived from its compressive load-displacement curve were found to be independent of temperature and MC, which helps benchmark material recovery in terms of veneer surface roughness/quality when manufacturing into quality plywood/LVL products. The method could also be applied to other wood composite elements such as wood strands.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".