The importance of engagement with fisheries, aquaculture, and Indigenous communities in the planning and implementation of marine carbon dioxide removal (mCDR)
Bibliographic record
Abstract
Abstract As climate change continues to increase in severity, the window of time available to achieve climate stabilization decreases. In addition to reducing emissions, climate solutions such as marine carbon dioxide removal (mCDR) are being considered. If mCDR is to scale from research to implementation it will impact various sectors including fisheries and aquaculture. Well-coordinated, co-developed deployments along with meaningful and early engagement between the mCDR and fisheries, aquaculture, and Indigenous communities can maximize opportunities to avert zero-sum trade-offs and increase the potential for mutually beneficial synergies between the various groups. Limited engagement with fisheries, aquaculture, and Indigenous communities may enhance the likelihood of community opposition, misinformation, potential ecosystem harm, and/or difficulty in weighing cost-benefits of mCDR approaches. At this early stage of research and development, mCDR initiatives can learn from other sectors and existing networks about best practices for engagement; however, this effort requires prioritization of intentional conversations. This perspective paper offers a brief overview of mCDR overlaps with fisheries and aquaculture, followed by insights about the current state of mCDR engagement with fisheries, aquaculture, and Indigenous communities. From our perspective as an interdisciplinary co-authorship team including members from academic and government sciences, Indigenous communities, and commercial fishing communities, we offer the following high-level recommendations for engagement across mCDR and fisheries, aquaculture, and Indigenous communities that are based on lessons learned in other sectors and research areas: synthesize and expand current state of knowledge; conduct early and meaningful engagement; leverage existing networks; establish strong interdisciplinary collaboration; co-design projects with communities; and develop frameworks and best practice guides.
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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 | 0.046 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".