Biomineralization Performance of <i>Bacillus sphaericus</i> under the Action of <i>Bacillus mucilaginosus</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microbial Induced Calcite Precipitation (MICP) is a biochemical process widely found in nature, also known as microbial mineralization. This paper investigates whether this process can help promote the intelligent reinforcement and repair of underground projects such as mines and tunnels. We selected Bacillus sphaericus and Bacillus mucilaginosus as the research objects. The former has an outstanding urease production ability, and the latter can secrete carbonic anhydrase in vitro. Bacillus mucilaginosus was introduced into the culture solution of Bacillus sphaericus in the most suitable culture environment, and the changes of mineralization rate and mineralization yield of Bacillus sphaericus were observed and analyzed. The results revealed that, to maintain the highest growth rate of Bacillus sphaericus , the optimal pH value was between 7 and 8, the optimal urea concentration was 0.5 mol/L, the optimal Ca 2+ concentration was 0.6 mol/L, and the optimal Luria‐Bertani (LB) culture concentration was 20 g/L. The amount of biomineralized calcium carbonate precipitated in the double bacteria solution can reach 1.89 times the amount of the precipitation in the Bacillus sphaericus solution under the same conditions. It concludes that the introduction of Bacillus mucilaginosus can effectively increase the mineralization yield of Bacillus sphaericus without affecting the mineralized products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it