Experimental Deployment of Microbial Mineral Carbonation at an Asbestos Mine: Potential Applications to Carbon Storage and Tailings Stabilization
Bibliographic record
Abstract
A microbial mineral carbonation trial was conducted at the Woodsreef Asbestos Mine (NSW, Australia) to test cyanobacteria-accelerated Mg-carbonate mineral precipitation in mine tailings. The experiment aimed to produce a carbonate crust on the tailings pile surface using atmospheric carbon dioxide and magnesium from serpentine minerals (asbestiform chrysotile; Mg3Si2O5(OH)4) and brucite [Mg(OH)2]. The crust would serve two purposes: Sequestering carbon and stabilizing the hazardous tailings. Two plots (0.5 m3) on the tailings pile were treated with sulfuric acid prior to one plot being inoculated with a cyanobacteria-dominated consortium enriched from the mine pit lakes. After 11 weeks, mineral abundances in control and treated tailings were quantified by Rietveld refinement of powder X-ray diffraction data. Both treated plots possessed pyroaurite [Mg6Fe2(CO3)(OH)16·4H2O] at 2 cm depth, made visible by its orange-red color. The inoculated plot exhibited an increase in the hydromagnesite [Mg5(CO3)4(OH)2·4H2O] content from 2–4 cm depth. The degree of mineral carbonation was limited compared to previous experiments, revealing the difficulty of transitioning from laboratory conditions to mine-site mineral carbonation. Water and carbon availability were limiting factors for mineral carbonation. Overcoming these limitations and enhancing microbial activity could make microbial carbonation a viable strategy for carbon sequestration in mine tailings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".