An evolution towards scientific consensus for a sustainable ocean future
Bibliographic record
Abstract
Abstract The ocean has recently taken centre stage in the global geopolitical landscape. Despite rising challenges to the effectiveness of multilateralism, attention to ocean issues appears as an opportunity to co-create pathways to ocean sustainability at multiple levels. The ocean science community, however, is not sufficiently well organised to advance these pathways and provide policy input. The Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services demonstrate how knowledge consensus and integration have been instrumental in charting global pathways and eliciting commitments to address, respectively, climate change and biodiversity loss. An equally impactful global platform with a thematic focus on ocean sustainability is needed. Here we introduce the International Panel for Ocean Sustainability (IPOS) as a coordinating mechanism to integrate knowledge systems to forge a bridge across ocean science-policy divides collectively. The IPOS will enrich the global policy debate in the Ocean Decade and support a shift toward ocean sustainability.
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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 | 0.098 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".