Direct Air Carbon Capture and Sequestration: How It Works and How It Could Contribute to Climate-Change Mitigation
Bibliographic record
Abstract
Owing to the small quantity of carbon dioxide (CO 2 ) that can be emitted before we exceed the 1.5°C–2°C target of the Paris Agreement on climate change, we are increasingly likely to require ways of removing significant CO 2 from the atmosphere. In addition to the biological options considered to date such as afforestation and bioenergy with CO 2 capture, direct air carbon capture and sequestration (DACCS) is emerging as a potentially important synthetic CO 2 removal technology. Here, we explain how DACCS works, focusing on two major processes that have been developed into large-scale pilot plants. We discuss cost estimates and operational energy requirements, as well as ecological and ethical considerations. We highlight the role of DACCS in the low-carbon transition by discussing its benefits, while also noting potential trade-offs and uncertainties that deserve further investigation.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".