Deepening the Decade: Collaborative Action for Advancing Deep‐Ocean Science and Policy in the United Nations Decade of Ocean Science for Sustainable Development
Bibliographic record
Abstract
Abstract The current United Nations Decade of Ocean Science for Sustainable Development (2021–2030; hereafter, the Decade) offers a unique opportunity and framework to globally advance ocean science and policy. Achieving meaningful progress within the Decade requires collaboration and coordination across Decade Actions (Programs, Projects, and Centres). This coordination is particularly important for the deep ocean, which remains critically under‐sampled compared to other ecosystems. Despite the limited sampling, the deep ocean accounts for over 95% of Earth's habitable space, plays a crucial role in regulating the carbon cycle and global temperatures, and supports diverse ecosystems. To collectively advance deep‐ocean science, we gathered representatives from 20 Decade Actions that focus at least partially on the deep ocean. We identified five broad themes that aim to advance deep‐ocean science in alignment with the Decade's overarching 10 Challenges: natural capital and the blue economy, biodiversity, deep‐ocean observing, best practices in data sharing, and capacity building. Within each theme, we propose concrete objectives (termed Cohesive Asks) and milestones (Targets) for the deep‐ocean community. Developing these Cohesive Asks and Targets reflects a commitment to better coordination across deep‐ocean Decade Actions. We aim to build bridges across deep‐ocean disciplines, which encompass natural science, ocean observing, policy, and capacity development.
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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 | 0.080 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".