Reactive Transport Modeling of Natural Carbon Sequestration in Ultramafic Mine Tailings
Bibliographic record
Abstract
Atmospheric CO 2 is naturally sequestered in ultramafic mine tailings as a result of the weathering of serpentine minerals [Mg 3 Si 2 O 5 (OH) 4 ] and brucite [Mg(OH) 2 ], and subsequent mineralization of CO 2 in hydrated magnesium carbonate minerals, such as hydromagnesite [Mg 5 (CO 3 ) 4 (OH) 2 ·4H 2 O]. Understanding the CO 2 trapping mechanisms is key to evaluating the capacity of such tailings for carbon sequestration. Natural CO 2 sequestration in subaerially exposed ultramafic tailings at a mine site near Mount Keith, Australia is assessed with a process‐based reactive transport model. The model formulation includes unsaturated flow, equations accounting for energy balance and vapor diffusion, fully coupled with solute transport, gas diffusion, and geochemical reactions. Atmospheric boundary conditions accounting for the effect of climate variations are also included. Kinetic dissolution of serpentine, dissolution‐precipitation of brucite and primary carbonates—calcite (CaCO 3 ), dolomite [MgCa(CO 3 ) 2 ], magnesite (MgCO 3 ), as well as the formation of hydromagnesite, halite (NaCl), gypsum (CaSO 4 ·2H 2 O), blödite [Na 2 Mg(SO 4 ) 2 ·4H 2 O], and epsomite [MgSO 4 ·7H 2 O]—are considered. Simulation results are consistent with field observations and mineralogical data from tailings that weathered for 10 yr. Precipitation of hydromagnesite is both predicted and observed, and is mainly controlled by the dissolution of serpentine (the source of Mg) and equilibrium with CO 2 ingressing from the atmosphere. The predicted rate for CO 2 entrapment in these tailings ranges between 0.6 and 1 kg m −2 yr −1 . However, modeling results suggest that this rate is sensitive to CO 2 ingress through the mineral waste and may be enhanced by several mechanisms, including atmospheric pumping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".