Predicting Atmospheric CO2 Sequestration in Ultramafic Mine Tailings using an Empirically-Derived Model
Bibliographic record
Abstract
Mineral carbonation is a spontaneous reaction which sequesters carbon dioxide in mineral form. Mining companies are interested in mineral carbonation because ultramafic mine tailings can undergo this reaction and sequester CO2. In partnership with Canada Nickel Company (CNC), a junior mining company located in Timmins, ON, this project set out to quantify CO2 sequestration in CNC’s mine tailings and model CO2 sequestration in their prospective tailings storage facility (TSF). A column experiment with 10 cm deep of CNC’s tailings determined the maximum 19 kg CO2 / t tailings sequestration capacity, reached between 56-112 days of reaction in the surface 0-1 cm layer. Reaction stopped both at the surface where the maximum conversion was reached and below the surface where reaction extent was significantly lower. Passivation of reaction throughout the column was attributed to cementation of the tailings surface from the mineral carbonation reaction, inhibiting CO2 ingress below the surface. Empirical relationships were developed for reaction passivation and depth in the column experiment. A second experiment developed empirical relationships for the effect of temperature and water saturation by estimating CO2 flux into the tailings. An empirical rate expression was proposed and used to model the rate of reaction as a function of these reaction parameters. CO2 sequestration was modelled for three scenarios: the column experiment for tuning the model, a static TSF with a singular deposition event, and a dynamic TSF with regular deposition. The column experiment results were reproduced by tuning the model to a baseline reaction rate of 0.7 kg CO2/t tailings/day at 30% water saturation in the 0-1 cm reaction layer. The static TSF scenario estimated a net 4 kg CO2 sequestered/t tailings between 0-10 cm deep from May to October. The dynamic tailings storage facility model predicted CO2 sequestration between May and October with varying deposition cycle rates. The dynamic TSF had an optimal deposition cycle rate of 14-16 days with a net 14 kg CO2 sequestered /t tailings between 0-10 cm deep.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".