Calcium carbonate precipitation by strain <i>Bacillus licheniformis</i><scp>AK</scp>01, newly isolated from loamy soil: a promising alternative for sealing cement‐based materials
Bibliographic record
Abstract
The relevant experiments were designed to determine the ability of indigenous bacterial strains isolated from limestone caves, mineral springs, and loamy soils to induce calcium carbonate precipitation. Among all isolates examined in this study, an efficient carbonate-precipitating soil bacterium was selected from among the isolates and identified by 16S rRNA gene sequences as Bacillus licheniformis AK01. The ureolytic isolate was able to grow well on alkaline carbonate-precipitation medium and precipitate calcium carbonate more than 1 g L(-1). Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) analyses, and scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDX) examinations were performed in order to confirm the presence of calcium carbonate in the precipitate and to determine which polymorphs were present. The selected isolate was determined to be an appropriate candidate for application in a surface treatment of cement-based material to improve the properties of the mortar. Biodeposition of a layer of calcite on the surface of cement specimens resulted in filling in pore spaces. This could be an alternative method to improve the durability of the mortar. The kind of bacterial culture and medium composition had a profound impact on the resultant CaCO(3) crystal morphology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".