Greenhills Dunite Subsurface Reaction Kinetics in a Global Mafic–Ultramafic Context
Bibliographic record
Abstract
The strong impetus to reduce emissions from fossil fuels has led to the development of abatement technologies, such as in situ carbon storage via mineralization in mafic and ultramafic rocks. While field-scale implementation of carbon storage in basaltic reservoirs is currently ongoing in Iceland, only pilot-level injections have been conducted in highly reactive ultramafic deposits in the Middle East. The South Island of New Zealand hosts numerous and extensive ultramafic rock bodies, including the intrusively deposited Greenhills Dunite within the Greenhills Ultramafic Complex. In this study, we evaluate the potential for carbon storage via in situ mineralization in Greenhills Dunite. An experimental matrix of batch reactions at subsurface temperatures and pressures shows a high carbonation extent across different conditions and size fractions, with the reaction products largely consisting of magnesite with evidence for iron incorporation. A 10-day high-pressure in situ X-ray diffraction experiment shows that the incipient stage of the reaction is dominated by brucite carbonation followed by the slower kinetics of olivine carbonation. The kinetics of these reactions were then compared to those in the broader (ultra)mafic carbonation literature that includes basalt and basaltic glass carbonation experiments. Overall, these results show potential for field-scale in situ mineralization deployment in the Greenhills Ultramafic Complex and highlights the potential for CO 2 storage in mafic–ultramafic rocks around the globe.
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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".