Carbonation, Cementation, and Stabilization of Ultramafic Mine Tailings
Bibliographic record
Abstract
Tailings dam failures can cause devastation to the environment, loss of human life, and require expensive remediation. A promising approach for de-risking brucite-bearing ultramafic tailings is in situ cementation via carbon dioxide (CO2) mineralization, which also sequesters this greenhouse gas within carbonate minerals. In cylindrical test experiments, brucite [Mg(OH)2] carbonation was accelerated by coupling organic and inorganic carbon cycling. Waste organics generated CO2 concentrations similar to that of flue gas (up to 19%). The abundance of brucite (2–10 wt %) had the greatest influence on tailings cementation as evidenced by the increase in total inorganic carbon (TIC; +0.17–0.84%). Brucite consumption ranged from 64–84% of its initial abundance and was mainly influenced by water availability. Higher moisture contents (e.g., 80% saturation) and finer grain sizes (e.g., clay-silt) that allowed for a better distribution of water resulted in greater brucite carbonation. Furthermore, pore clogging and surface passivation by Mg-carbonates may have slowed brucite carbonation over the 10 weeks. Unconfined compressive strengths ranged from 0.4–6.9 MPa and would be sufficient in most scenarios to adequately stabilize tailings. Our study demonstrates the potential for stabilizing brucite-bearing mine tailings through in situ cementation while sequestering CO2.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".