Determination of the Liquid Water Diffusivity from Transient Moisture Transfer Experiments
Bibliographic record
Abstract
The Boltzmann transformation method is used to determine the liquid water diffusivity from moisture content profiles as measured in a capillary water absorption experiment. An inter-laboratory comparison for analyzing the reliability of the determination method showed that the inaccuracy in the liquid water diffusivity is caused by scatter in the transformed data and by uncertainty in the boundary conditions at the intake surface and ahead of the steep moisture front. A methodology is proposed based on (1) the evaluation of the validity of the diffusion approach, (2) a simplified handling of the boundary conditions, (3) smoothing of the scattered data and (4) the evaluation of the quality of the determined liquid water diffusivity. For HAM (Heat-Air-Moisture transport) calculations values of the liquid water diffusivity for moisture contents higher than the capillary moisture content are disregarded. The liquid water diffusivity can be described by an exponential function limited at a lower moisture content bound. To describe the moisture diffusivity including liquid water and water vapour transports, a new parametric description of the moisture diffusivity is presented, which shows sufficient flexibility both in the hygroscopic and overhygroscopic ranges. When permeability is calculated from diffusivity, the permeability should monotonically increase with decreasing capillary pressure. In the hygroscopic region it should coincide with the measured water vapour permeabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".