Co-designing the global ocean observing system for service delivery
Bibliographic record
Abstract
Ocean data from systematic observations are the foundation for national, regional and global action. Ocean data underpins progress across many multilateral agreements including the UNFCCC and Paris Agreement, United Nations agreement on biodiversity beyond national jurisdiction, CBD Kunming-Montreal Global Biodiversity Framework, IMO, FAO, UNEP and the plastic treaty.There is an urgent need for nations to strengthen and expand the global ocean observing system (GOOS) to build a sustained and sustainable critical ocean observing infrastructure, that delivers data at national, regional and global levels. Yet ocean observing networks and data systems are not recognized as critical infrastructure and often reliant on scientific research funding.The strengthening and expansion of the global ocean observing system must be built from key advancements in and vision for ocean observing set in place by the Framework for Ocean Observing, the GOOS Strategy 2030, as well as more recently by the Ocean Decade Challenge 7 to Sustainably expand the Global Ocean Observing System.We need to codesign the system and codeliver the services ensuring ocean observations as the raw ingredients for the value chain for, amongst other things, forecasting and early warning systems for multi-hazard risks, marine protection and safety, biodiversity positive resilient communities, climate change mitigation and adaptation, and sustainable ocean economy as well as understanding of the Earth system and indicators to inform decision making and policy.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.024 |
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".