A Roadmap for Using the UN Decade of Ocean Science for Sustainable Development in Support of Science, Policy, and Action
Bibliographic record
Abstract
The health of the ocean, central to human well-being, has now reached a critical point. Most fish stocks are overexploited, climate change and increased dissolved carbon dioxide are changing ocean chemistry and disrupting species throughout food webs, and the fundamental capacity of the ocean to regulate the climate has been altered. However, key technical, organizational, and conceptual scientific barriers have prevented the identification of policy levers for sustainability and transformative action. Here, we recommend key strategies to address these challenges, including (1) stronger integration of sciences and (2) ocean-observing systems, (3) improved science-policy interfaces, (4) new partnerships supported by (5) a new ocean-climate finance system, and (6) improved ocean literacy and education to modify social norms and behaviors. Adopting these strategies could help establish ocean science as a key foundation of broader sustainability transformations.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.068 | 0.015 |
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".