Ambient weathering of magnesium oxide for CO2 removal from air
Bibliographic record
Abstract
Abstract To avoid dangerous climate change, new technologies must remove billions of tonnes of CO 2 from the atmosphere every year by mid-century. Here we detail a land-based enhanced weathering cycle utilizing magnesite (MgCO 3 ) feedstock to repeatedly capture CO 2 from the atmosphere. In this process, MgCO 3 is calcined, producing caustic magnesia (MgO) and high-purity CO 2 . This MgO is spread over land to carbonate for a year by reacting with atmospheric CO 2 . The carbonate minerals are then recollected and re-calcined. The reproduced MgO is spread over land to carbonate again. We show this process could cost approximately $46–159 tCO 2 −1 net removed from the atmosphere, considering grid and solar electricity without post-processing costs. This technology may achieve lower costs than projections for more extensively engineered Direct Air Capture methods. It has the scalable potential to remove at least 2–3 GtCO 2 year −1 , and may make a meaningful contribution to mitigating climate change.
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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".