The Mining Industry’s Role in Enhanced Weathering and Mineralization for CO<sub>2</sub> Removal
Bibliographic record
Abstract
Enhanced weathering and mineralization (EWM) aim to remove carbon dioxide (CO 2 ) from the atmosphere by accelerating the reaction of this greenhouse gas with alkaline minerals. This suite of geochemical negative emissions technologies has the potential to achieve CO 2 removal rates of >1 gigatonne per year, yet will require gigatonnes of suitable rock. As a supplier of rock powder, the mining industry will be at the epicenter of the global implementation of EWM. Certain alkaline mine wastes sequester CO 2 under conventional mining conditions, which should be quantified across the industry. Furthermore, mines are ideal locations for testing acceleration strategies since tailings impoundments are contained and highly monitored. While some environmentally benign mine wastes may be repurposed for off-site use─reducing costs and risks associated with their storage─numerous new mines will be needed to supply rock powders to reach the gigatonne scale. Large-scale EWM pilots with mining companies are required to progress technology readiness, including carbon verification approaches. With its knowledge of geological formations and ore processing, the mining industry can play an essential role in extracting the most reactive rocks with the greatest CO 2 removal capacities, creating supply chains, and participating in life-cycle assessments. The motivations for mining companies to develop EWM include reputational benefits and carbon offsets needed to achieve carbon neutrality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".