Strategizing Carbon-Neutral Mines: A Case for Pilot Projects
Bibliographic record
Abstract
Ultramafic and mafic mine tailings are a valuable feedstock for carbon mineralization that should be used to offset carbon emissions generated by the mining industry. Although passive carbonation is occurring at the abandoned Clinton Creek asbestos mine, and the active Diavik diamond and Mount Keith nickel mines, there remains untapped potential for sequestering CO2 within these mine wastes. There is the potential to accelerate carbonation to create economically viable, large-scale CO2 fixation technologies that can operate at near-surface temperature and atmospheric pressure. We review several relevant acceleration strategies including: bioleaching of magnesium silicates; increasing the supply of CO2 via heterotrophic oxidation of waste organics; and biologically induced carbonate precipitation, as well as enhancing passive carbonation through tailings management practices and use of CO2 point sources. Scenarios for pilot scale projects are proposed with the aim of moving towards carbon-neutral mines. A financial incentive is necessary to encourage the development of these strategies. We recommend the use of a dynamic real options pricing approach, instead of traditional discounted cash-flow approaches, because it reflects the inherent value in managerial flexibility to adapt and capitalize on favorable future opportunities in the highly volatile carbon market.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".