Report on the Future Contribution of Ocean NETs in Different Climate Policies
Bibliographic record
Abstract
This report explores the future contribution of ocean-based Nega- tive Emissions Technologies (NETs) within various climate policy frameworks, evaluating their economic and environmental viability and potential cost- effectiveness. It emphasizes the need to integrate CDR into emissions trading systems like the EU ETS. Investment in pilot projects is crucial for better cost estimation and evaluating environmental co-benefits and risks, highlighting the importance of a comprehensive portfolio of research and development and early deployment subsidies, particularly for Ocean Alkalinity Enhancement, ocean fertilization, and potentially artificial upwelling. Marine permaculture, specif- ically macroalgae cultivation and harvest, is identified as a promising NET, with simulations demonstrating its potential to meet Nationally Determined Contributions in regions with high abatement costs and ambitious emission targets, such as the EU, Japan, Canada, and Great Britain. Effective moni- toring and compliance systems are critical for ensuring CDR project integrity, and international collaboration is essential for enhancing NET effectiveness. (OceanNets Deliverable, D1.7)
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".