A 3D visualization method for identifying fabric characteristics during suffusion using transparent soil
Bibliographic record
Abstract
Suffusion is a process by which fine particles move through the voids between coarse particles (intergranular voids) by seepage flow. Therefore, identifying the arrangement of coarse particles (coarse matrix), intergranular voids, and the spatial distribution of fine particles is a key issue for studying suffusion. Transparent soil is being increasingly used to replace real soil in laboratory model tests for studying the internal particle movements and pore fluid flow in soils. This study established an experimental setup for scanning a transparent soil specimen during seepage by a moving laser and made an attempt to construct a 3D digital mesostructural model of the specimen based on the images of the sequentially illuminated cross-sections. An example test on a gap-graded soil was conducted to show the performance of the presented method. It demonstrated that the coarse matrix and intergranular voids can be visualized and quantified at satisfactory precisions; moreover, the loss and redistribution of fine particles as well as important phenomena such as deposition at pore bodies and clogging of pore throats can also be visualized and identified qualitatively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".