Development of a reactive transport model for microbial induced calcium carbonate precipitation in unsaturated conditions
Bibliographic record
Abstract
Microbial induced calcium carbonate precipitation (MICP) offers a sustainable technique to improve geologic properties of soils in engineering structures. The applications encompass improved soil strength, scour mitigation, fracture sealing, and in situ contaminant immobilization. Previous studies have presented fundamental processes and implementation in lab- and field-scale. Most of these studies were examined in saturated conditions despite many MICP applications including those in coastal and riverside areas which will likely take place under unsaturated conditions. The study herein investigated the effect of soil water retention curve (SWRC) parameters and attachment coefficient ( Kat ) on CaCO3 precipitation in sand. Using numerical analyses, a continuum model was developed in which unsaturated flow and transport were coupled with biological and chemical reactions in variably saturated conditions. Predictive modeling results compare mass percentage of calcium carbonate resulting from MICP at degrees of soil water saturations of 20%, 40%, 80%, and 100% in sandy soil media. The results indicate the bacteria attachment coefficient increases by a factor of 3 as the degree of saturation is decreased from 100% to 20%, as the higher suctions at lower saturation levels improve bacteria fixation. The drying branch of SWRC versus wetting front yields higher CaCO3 for identical MICP treatment. Numerical results show the trend in hydraulic conductivity with increasing cementation level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".