Optimisation of microbially induced calcite precipitation protocol against erosion
Bibliographic record
Abstract
Five strategies of microbially induced calcite precipitation (MICP) are suggested to improve the uniformity of treatment and enhance the erosional/hydraulic response of internally unstable and poorly graded soils. The treatment consisted of the injection of a bacterial suspension and a cementation solution. The volume of the injected bacterial suspension varied from 0·3 to one pore volume, while the concentration of the cementation solution (urea/calcium chloride (CaCl 2 )) varied from 0·35 to 0·75 M. The biotreatment of sand columns (ϕ = 160 mm and h = 100 mm) was conducted in either one, two or three stages, with or without a low-salinity fixation solution. The biotreated specimens were then submitted to an erosion test, and the results show that alternating injection of reagents in three stages enhances the uniformity of biomineralisation, as the hydraulic conductivity is constant from the top to the bottom of the specimens. This protocol also prevents internal erosion as the critical hydraulic gradient (i cr ) is equal to 10 and the cumulative fine loss (M e ) does not exceed 63 g/m 2 . Finally, the use of a fixation solution prevents any clogging near the injection point and stimulates the bacterial transport in the soil.
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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".