Technoeconomic Potential for Carbon Mineralization with Enhanced Recovery of Critical Minerals in the Pacific Northwest
Bibliographic record
Abstract
Commitments to limit the effects of global climate change require the implementation of renewable energy, along with a significant reduction of CO 2 emissions. To facilitate this transition, the active removal of atmospheric CO 2 by the development and deployment of new carbon management infrastructure and technologies is needed. Additionally, the world is projected to require an unprecedented amount of critical minerals, yet current high-grade ore supplies and existing mining technologies are unable to meet this demand. This study analyzes the feasibility of a novel CO 2 mineralization and enhanced mineral recovery (CO 2 -EMR) technology designed to target low-grade, historically uneconomical resources for in situ mining. The Josephine Ophiolite in Northern California and Twin Sisters Dunite in Northwest Washington are promising ultramafic reservoirs for implementing this new mining technology. Ultramafic, olivine-rich rocks from these two sites were characterized pre- and postreaction with CO 2, with both samples showing rapid carbonation in the form of magnesite. Furthermore, the fluid sampled shows a high recovery of nickel, a designated critical mineral. These experimental findings were then implemented in a technoeconomic analysis to assess the viability of field-scale implementation of this technology in the Pacific Northwest and beyond.
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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".