Mineral Carbonation Potential (MCP) of Mine Waste Material: Derivation of an MCP Parameter
Bibliographic record
Abstract
The heterogenous mineralogy of ultramafic deposits hosting mining operations makes it challenging to accurately determine the waste rock’s mineral carbonation potential (MCP). Additionally, the significantly higher carbonation capabilities of olivine than serpentine add to the difficulty. To address this issue, in this work, a new and unique tool called the MCP calculator was developed as a Microsoft ExcelTM spreadsheet to accurately determine the amount of anthropogenic CO2 that a given rock mass can sequester through mineral carbonation. The program estimates the modal mineral abundance of ultramafic rocks to aid in MCP estimation. This tool is designed to be cost-effective and tailored for use by the mining industry, utilising abundant lithogeochemical data to evaluate their deposit as a potential substrate for industrial mineral carbonation operations. The paper introduces the MCP calculator, outlines a framework for developing the MCP parameter, and presents an example of its application. The calculator is specific to the mineral assemblage investigated at the Turnagain ultramafic complex in northern British Columbia but can be adjusted to study comparable deposits. The paper acknowledges that using waste rock in a mineral carbonation operation requires economic and practical decisions beyond the scope of the research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".