Carbonate Precipitation of Bacterial Strains Isolated from Sediments and Seawater: Formation Mechanisms
Bibliographic record
Abstract
This article presents a research study on carbonate formation in solid and liquid media by Thalassospira sp., Halomonas sp., Bacillus pumilus, and Pseudomonas grimontii, four bacterial strains isolated from sediments and deep seawater. As part of this study, we analyzed carbonic anhydrase activity, pH, adsorption of calcium and magnesium ions, and total organic and inorganic carbon. The geochemical program PHREEQC was also used to calculate the mineral saturation indexes in all the cultures. The minerals formed were studied with X-ray diffraction, X-ray dispersive energy microanalysis, and scanning electron microscopy. In addition, all four bacterial strains were found to induce carbonate precipitation and to have carbonic anhydrase activity. Sterile control experiments did not precipitate carbonate. In solid M1 and B4 media, all of the strains precipitated magnesium calcite, whereas in the liquid media, they precipitated different percentages of magnesium calcite, aragonite, and monohydrocalcite. In both cases, small amounts of amorphous precipitates were also produced. This article discusses carbonate formation and the possible role played by metabolic activity, bacterial surfaces and carbonic anhydrase in this process. Finally, the results obtained lead to a hypothesis regarding the importance of carbonate precipitation for the survival of bacteria populations in certain habitats.
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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 | 0.001 | 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".