Carbon Sequestration Kinetic and Storage Capacity of Ultramafic Mining Waste
Bibliographic record
Abstract
Mineral carbonation of ultramafic rocks provides an environmentally safe and permanent solution for CO(2) sequestration. In order to assess the carbonation potential of ultramafic waste material produced by industrial processing, we designed a laboratory-scale method, using a modified eudiometer, to measure continuous CO(2) consumption in samples at atmospheric pressure and near ambient temperature. The eudiometer allows monitoring the CO(2) partial pressure during mineral carbonation reactions. The maximum amount of carbonation and the reaction rate of different samples were measured in a range of experimental conditions: humidity from dry to submerged, temperatures of 21 and 33 °C, and the proportion of CO(2) in the air from 4.4 to 33.6 mol %. The most reactive samples contained ca. 8 wt % CO(2) after carbonation. The modal proportion of brucite in the mining residue is the main parameter determining maximum storage capacity of CO(2). The reaction rate depends primarily on the proportion of CO(2) in the gas mixture and secondarily on parameters controlling the diffusion of CO(2) in the sample, such as relative saturation of water in pore space. Nesquehonite was the dominant carbonate for reactions at 21 °C, whereas dypingite was most common at 33 °C.
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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) | 0.000 | 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) | 0.001 | 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".