Biomineralisation performance of bacteria isolated from a landfill in China
Bibliographic record
Abstract
We report an investigation of microbially induced carbonate precipitation by seven indigenous bacteria isolated from a landfill in China. Bacterial strains were cultured in a medium supplemented with 25 mmol/L calcium chloride and 333 mmol/L urea. The experiments were carried out at 30 °C for 7 days with agitation by a shaking table at 130 r/min. Scanning electron microscopic and X-ray diffraction analyses showed variations in calcium carbonate polymorphs and mineral composition induced by all bacterial strains. The amount of carbonate precipitation was quantified by titration. The amount of carbonate precipitated in the medium varied among isolates, with the lowest being Bacillus aerius rawirorabr15 (LC092833) precipitating around 1.5 times more carbonate per unit volume than the abiotic (blank) solution. Pseudomonas nitroreducens szh_asesj15 (LC090854) was found to be the most efficient, precipitating 3.2 times more carbonate than the abiotic solution. Our results indicate that bacterial carbonate precipitation occurred through ureolysis and suggest that variations in carbonate crystal polymorphs and rates of precipitation were driven by strain-specific differences in urease expression and response to the alkaline environment. These results and the method applied provide benchmarking and screening data for assessing the bioremediation potential of indigenous bacteria for containment of contaminants in landfills.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".