The need to explore the potential of marine CDR – A guide for policy makers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Limiting global warming to 1.5°C or 2°C in the midst of a climate emergency requires rapid, deep, and sustained emission reductions, alongside annual CDR at the billion-tonne (Gt) scale. CDR is essential for addressing hard-to-abate, residual emissions and reducing atmospheric CO₂. Achieving the billion-tonne CDR target demands a holistic approach that includes both land and ocean – which we term One-Earth CDR. One-Earth CDR is critical because all CDR methods face a "CDR tax" due to feedbacks from the human-altered Earth System. These feedbacks release stored anthropogenic CO₂ from land and ocean reservoirs, which partially offsets the effectiveness of CDR. Therefore, to reach the billion-tonne goal, CDR must be applied sustainably in all feasible environments. One-Earth CDR also serves as a safeguard against over-reliance on land-based CDR, which faces challenges such as side effects (e.g., mega-fires) and sustainability limits (e.g., land and water use). Marine CDR (mCDR) using innovative methods offers a large potential for carbon storage. Proving the effectiveness and safety of mCDR will likely take at least a decade. Ensuring its integrity is crucial for verifiable CDR. Before large-scale deployment, knowledge gaps must be addressed, including risks, sustainability, scalability, cost, permanence, side effects, monitoring, verification, social acceptance, and governance frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.003 | 0.007 |
| 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 it