Bio-Cementation via Microbially Induced Calcium Carbonate Precipitation for Surface Applications: The Effects of Sand Particle Size on Uniformity and Strength
Bibliographic record
Abstract
The use of biological methods to improve the mechanical characteristics of geomaterials has gained popularity recently. The utilization of the enzyme “urease,” when produced by a microbe, which causes the breakdown of urea and leads to the precipitation of calcium carbonate (CaCO3) when mixed with calcium, is known as microbially induced calcium carbonate precipitation (MICP). MICP is a promising approach for surface soil strengthening. Previous studies have shown that finer soil contents can affect the uniformity of bio-cementation distribution through the soil sample, and thus its strength properties. The objective of this study was to analyze bio-cementation via MICP in various sand mediums, ranging from coarse to fine particle size and ranging from uniformly to well-graded. Sakrete medium commercial sand, Ottawa sand, and silica silt were used at different percentages to produce samples for treatment. Results conclude that well-graded sand compositions created stronger, more brittle samples than compared to poor-graded compositions and larger particle sand sizes. Additional testing needs to be done on finer sand particles as well as current sands to verify previous results before further testing can be done on varying soil compositions.
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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".