Validation of a Relative Permeability Model for Bioclogging in Unsaturated Soils
Bibliographic record
Abstract
Biological clogging of unsaturated soils is an important process that can lead to the development of a biomat and failure of biofilters used to treat various wastewater streams. Several conceptual models have been developed to simulate clogging in saturated soils; however, efforts to develop similar models for unsaturated soils have been limited. Recently, Mostafa and Van Geel proposed three conceptual models to simulate bioclogging in unsaturated systems. These models included the impact of biomass growth on the relative permeability term for unsaturated flow. The models were incorporated into a one‐dimensional unsaturated flow and transport numerical model that simulates biological clogging in which microbial growth was simulated using Monod kinetics. The conceptual models have not been validated with experimental data. In this study, column experiments were conducted to study the clogging process in three different sand soils; filter media sand, concrete sand, and septic bed sand. Monod kinetic parameters were also evaluated in a separate experiment for the same feed solution used in the column study. The aim of this study was to validate the conceptual models proposed by Mostafa and Van Geel. Significant improvements to the flow and transport numerical model were required to accommodate the laboratory conditions. Improvements included implementing a minimum relative permeability of the biomat layer and a more detailed description of the biomass structure, which includes active biomass, extracellular polymeric substances (EPS), and an inert fraction. Comparisons between experimental data and numerical simulations indicated that the improved conceptual model for relative permeability appears to be appropriate for modeling bioclogging in the experiments considered in this study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".