Accelerated mineral bio-carbonation of coarse residue kimberlite material by inoculation with photosynthetic microbial mats
Bibliographic record
Abstract
Microbiological weathering of coarse residue deposit (CRD) kimberlite produced by the Venetia Diamond Mine, Limpopo, South Africa enhanced mineral carbonation relative to untreated material. Cultures of photosynthetically enriched biofilm produced maximal carbonation conditions when mixed with kimberlite and incubated under near surface conditions. Interestingly, mineral carbonation also occurred in the dark, under water-saturated conditions. The examination of mineralized biofilms in ca. 150 µm-thick-sections using light microscopy, X-ray fluorescence microscopy (XFM) and backscatter electron-scanning electron microscopy-energy dispersive x-ray spectrometry demonstrated that microbiological weathering aided in producing secondary calcium/magnesium carbonates on silicate grain boundaries. Calcium/magnesium sulphate(s) precipitated under vadose conditions demonstrating that evaporites formed upon drying. In this system, mineral carbonation was only observed in regions possessing bacteria, preserved within carbonate as cemented microcolonies. 16S rDNA molecular diversity of bacteria in kimberlite and in natural biofilms growing on kimberlite were dominated by Proteobacteria that are active in nitrogen, phosphorus and sulphur cycling. Cyanobacteria based enrichment cultures provided with nitrogen & phosphorus (nutrients) to enhance growth, possessed increased diversity of bacteria, with Proteobacteria re-establishing themselves as the dominant bacterial lineage when incubated under dark, vadose conditions consistent with natural kimberlite. Overall, 16S rDNA analyses revealed that weathered kimberlite hosts a diverse microbiome consistent with soils, metal cycling and hydrocarbon degradation. Enhanced weathering and carbonate-cemented microcolonies demonstrate that microorganisms are key to mineral carbonation of kimberlite.
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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 | 0.000 | 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) | 0.001 | 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".