Evaluation of the Effectiveness of a Soil Treatment Using Calcium Carbonate Precipitation from Cultivated and Lyophilized Bacteria in Soil’s Compaction Water
Bibliographic record
Abstract
Microbial-induced carbonate precipitation (MICP) is a bio-inspired solution where bacteria metabolize urea to precipitate. This carbonate acts as a bio-cement that bonds soil particles. The existing framework has focused mainly on applying MICP through infiltration of liquid bacterial solutions in existing soil deposits. However, this technique is inefficient in soils with high fines content and low hydraulic conductivity, and thus few studies have focused on the use of MICP in fine soils. The main objective of this study was to evaluate the effect of MICP applied to compaction water in soils containing expansive clays and sandy silts. This approach searches for a better distribution of bacteria, nutrients, and calcium sources and is easy to apply if associated with a compaction process. In soils with expansive minerals, the effect of MICP in swelling potential was explored at laboratory and field scales. In sandy silts, the evolution of the stiffness and strength were studied at the laboratory scale. The treatment at the laboratory scale reduced the swelling potential; nevertheless, no significant effect of MICP was found in the field test. In sandy silts, the strength and stiffness increased under unsaturated conditions; however, subsequent saturation dissolved the cementation and the improvement vanished.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".