Stochastic modeling of kaolinite transport through a sand filter
Bibliographic record
Abstract
Accurately modeling the transport of clay particles through coarse-grained porous media is essential to engineering applications ranging from filtration and drainage, groundwater flow modeling, to contaminant transport. However, predicting the retention and clogging behavior of clay particles within a coarse-grained soil matrix is extremely challenging because clay particles can aggregate and form clusters with a variety of fabrics depending on the prevailing geochemistry of the pore fluid (i.e., pH and ionic strength). The work performed in this study developed a stochastic model to investigate the uncertainty of clay particle transport in porous media using random sampling at a given grain-size distribution to account for inherent uncertainty of the size of clay clusters being transported. Results demonstrated that the model proposed in this work can evaluate upper and lower boundaries of retention profiles of clay particles in a sand medium at given mean and standard deviation of grain-size distributions. In addition, the deterministic approach (using median sizes of sand and clay particles in the simulation) underestimated the mass of retained particles at small size ratios of clay particle size/sand particle size when compared with the stochastic prediction, which would result in nonconservative design.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".