Linear Expansion And Thickness Swell Of Mdf As A Function Of Panel Density And Sorption State
Bibliographic record
Abstract
Experiments were conducted using ASTM standard methods to determine the medium density fiberboard (MDF) expansion properties and swelling characteristics as a function of panel density and sorption state. Specimens without density profile were produced by removing the surface layers of laboratory MDF panels. The results from the trials showed that for laboratory MDF, linear expansion is homogenous in panel plane. When specimen density increased, linear expansion, linear expansion coefficient, thickness shrinkage coefficient, linear contraction, and linear contraction coefficient increased. Thickness swell was higher than thickness shrinkage at any density level. Thickness swell coefficient was higher than thickness shrinkage coefficient for low density levels. The values of linear contraction and linear contraction coefficient (in desorption) were higher than the values of linear expansion and linear expansion coefficient (in adsorption). The values on thickness swell and thickness shrinkage were much higher than the values of linear expansion and linear expansion and linear contraction at any density level. The effect of density on linear expansion, linear expansion coefficient and linear contraction coefficient was significantly stronger than the effect of density on thickness swell, thickness swell coefficient and thickness shrinkage.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".