Visualization of diffusion phenomena in porous media by means of X-ray computed tomography (CT) scanning
Bibliographic record
Abstract
The migration of water and contamination materials in rock structures is a significant issue in projects that utilize deep underground locations such as “carbon-dioxide capture and storage” (CCS) and disposal of high-level nuclear waste. These phenomena are also important in the area of preservation of stone structures of cultural heritage signficance, as such stone structures are usually located outside, exposed to wind and rain. The migration of contamination materials in the underground environment, especially in porous rock mass, is governed mainly by water permeation and diffusion. In this study, one-dimensional diffusion testing was conducted and the process in the porous materials was visualized by X-ray computed tomography (CT) scanning. Diffusion is the process caused by the concentrate or density gradient, and is a suitable phenomenon for X-ray CT analysis. In this paper, information related to diffusion is extracted from X-ray CT image data and the distribution of concentration is estimated. From the obtained density distribution, diffusion coefficients are evaluated. One-dimensional permeation tests were also conducted and intrinsic permeabilities of porous materials are evaluated, then the relation between diffusion coefficients and intrinsic permeability is discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".