Sand and silty-sand soil stabilization using bacterial enzyme–induced calcite precipitation (BEICP)
Bibliographic record
Abstract
This paper examines the bio-derived stabilization of sand-only or sand-plus-silt soils using an extracted bacterial enzyme application to achieve induced calcite precipitation (ICP). As compared to conventional microbial induced calcite precipitation (MICP) methods, which use intact bacterial cells, this strategy that uses free urease catalysts to secure bacterial enzyme–induced calcite precipitation (BEICP) appears to offer an improved means of bio-stabilizing silty-sand soils as compared to that of MICP processing. Several benefits may possibly be achieved with this BEICP approach, including bio-safety, environmental, and geotechnical improvements. Notably, the BEICP bio-stabilization results presented in this paper demonstrate (i) higher rates of catalytic urease activity, (ii) a wider range of application with sand-plus-silt soil applications bearing low-plasticity properties, and (iii) the ability to retain higher levels of soil permeability after BEICP processing. Comparative BEICP versus MICP results for sand-only systems are presented, along with BEICP-based results for stabilized soil mixtures at 90:10 and 80:20 percentile sand:silt ratios. This BEICP method’s ability to obtain unconfined compressive strength results in excess of 1000 kPa with sand-plus-silt soil mixtures is particularly noteworthy.
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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".