Climate targets, carbon dioxide removal and the potential role of Ocean Alkalinity Enhancement
Bibliographic record
Abstract
Abstract. The Paris Agreement to limit global warming to well below 2 °C requires ambitious emission reduction and the balancing of remaining emissions through carbon sinks, i.e. the deployment of carbon dioxide removal (CDR). While ambitious climate protection scenarios until now consider primarily land-based CDR methods, there is growing concern about their potential to deliver sufficient CDR, and marine CDR options receive more and more interest. Based on idealized theoretical studies, Ocean Alkalinity Enhancement (OAE) appears as a promising marine CDR method. However, the knowledge base is insufficient for a robust assessment of its practical feasibility, of its side effects, social and governance aspects as well as monitoring, reporting and verification issues. A number of research efforts aim to improve this in a timely manner. We provide an overview on the current situation of developing OAE as marine CDR method, and describe the history that has led to the creation of the OAE research Best Practices Guide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".