Influence of bacterial suspension type on the strength of biocemented sand
Bibliographic record
Abstract
Soil properties, the chemical composition of cementation solution, injection technique, and environmental conditions have all been extensively studied as variables influencing microbially induced carbonate precipitation performance. However, despite the fact that different researchers have used different bacterial suspensions, the influence of bacterial suspension type, more specifically some organic matters in bacterial suspensions, which can play a key role in the morphology and mechanical properties of CaCO3, has often been overlooked. In this study, the harvested bacterial solutions were centrifuged to separate bacterial cells and supernatant. The precipitated cells were then diluted with three distinct solutions: supernatant (RB), fresh culture medium (FB), and 0.9% NaCl solution (NB), which were subsequently utilized to stabilize the sand. The results indicated that the bacterial suspension type could greatly impact the strength of biocemented sand, particularly coarse and medium sand. Differences in unconfined compressive strength can be related to differences in precipitated CaCO3 microstructures, morphologies, and compositions, which were examined using scanning electron microscopy, X-ray diffraction analysis, and Fourier transform infrared spectroscopy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".