The effect of mineral composition on direct aqueous carbonation of ultramafic mine waste rock for CO<sub>2</sub> sequestration, a case study of Turnagain ultramafic complex in British Columbia, Canada
Bibliographic record
Abstract
The heterogenous mineralogy of ultramafic mine waste rocks make their carbonation rate hard to evaluate. This study investigated the effect of mineral composition on direct aqueous carbonation, through quantitative X-ray diffraction. The results show that magnesite was the main carbonated product, which was a stoichiometric conversion of forsterite. Diopside acted as a seed for quick crystallisation of magnesite. Lizardite could enhance the carbonation conversion, which reaches the optimum value when the weight ratio of lizardite to forsterite is 0.3–0.4. Sulphide in the reactant samples has a positive influence on carbonation. Brucite, magnetite and maghemite show little influence on carbonated products.
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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 | 0.001 | 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) | 0.000 | 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".