Microbially Induced Calcium Carbonate Precipitation as a Carbon Sequestration Technique for Mining Waste
Bibliographic record
Abstract
The mining industry is responsible for a surplus of greenhouse gas (GHG) emissions, specifically carbon dioxide (CO2) emissions in the atmosphere. These emissions impact global warming and climate change creating environmental and social implications. It is, therefore, imperative to offset these emissions using carbon sequestration techniques. Both abiotic and biotic carbonation can trap atmospheric CO2 as carbonate (CO32−) precipitates (e.g., calcite, magnesite, and dolomite). However, biotic processes can enhance these chemical reactions using microorganisms as a catalyst. Microbially Induced Calcium Carbonate Precipitation (MICP) is a biotic process that utilizes several different metabolic pathways and microorganisms to facilitate precipitation. In addition to the precipitation of CaCO3 (mineral trapping), MICP uses geologic and solubility trapping mechanisms to sequester atmospheric CO2, which can be further optimized using carbon capture and storage (CCS) with CO2 injection. This process is feasible with mining and metalliferous waste and shows significant potential as a carbon sequestration technique to the mining industry.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".