Effect of Panel Moisture Content and Density on Moisture Movement in MDF
Bibliographic record
Abstract
This study examined the effect of medium density fiberboard (MDF) density and sorption state on the aorption isotherms and the effect of panel moisture content and density on the effective watcr conductivity and diffusion coefficient of MDF panels.A total of 39 laboratory-made MDF panels with dimensions 650 mm X 650 mm X 12 mm divided into 3 density groups (540 kg/m3, 650 kglm', and 800 kg/m7) was produced.The surface layers of each panel were removed, and the thickness of the remaining core layer of homogeneous density was reduced by sanding to 6 mm.The sorption isotherms were determined by exposing MDF samples to controlled relative humidities.The effective water conductivity was determined by the instantaneous profile method.Medium density fiberboard from all density levels displayed a marked sorption hysteresis.Both in adsorption and in desorption, the MDF specimens with higher density levels equilibrated at higher levels of moisture content.Moisture content had a stronger effect than density on the effective water conductivity.In desorption, the higher the moisture content level, the higher the effective water conductivity.Conversely, in adsorption, the effective water conductivity decreased as moisture content increased.The effective water conductivity in desorption and the diffusion coefficients both in desorption and adsorption were significantly higher for panels with a density of 540 kg/m3 than for densities of 650 and 800 kg/m3.In adsorption, the effective watcr conductivity of panels with a density of 540 kg/m3 was significantly higher than for a density of 800 kg/m'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".