Early Career Recommendations for the Equitable Growth of a Marine Carbon Dioxide Removal Sector
Bibliographic record
Abstract
Abstract In addition to steep carbon emission reductions, all modeled pathways to reach global climate goals require carbon removal. Marine carbon dioxide removal has the potential to play a large role in drawing down legacy anthropogenic emissions due to the scalability and durability of proposed methods. While this field is rapidly expanding, a number of issues remain, including efforts to grow the industry, align projects with equity and justice goals, and ensure development of trusted, unique, durable carbon credits. We, a group of early career ocean professionals (ECOPs), provide an overview of the scale of the field, the aforementioned issues, and then make recommendations to ensure global equity and expand early career capacity in the marine carbon dioxide removal sector. We argue that substantial investment is needed to reduce costs of marine carbon dioxide removal and spur innovation in monitoring, reporting, and verification, but also in the training and development of early career researchers. Careful co‐design of marine removal projects by experienced and emerging collaborators, including local communities, can help mitigate perpetuating existing global inequalities. Given the anticipated growth of the marine carbon dioxide removal workforce, ECOPs can contribute their existing interdisciplinary expertise, if they are supported within traditional structures. Those entering the field can leverage skill sets that intersect engineering, policy, community engagement, and business. We maintain that ECOPs will be key leaders in the field, if appropriately engaged, compensated, and empowered.
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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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.054 | 0.017 |
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".