What We Have Learned From the Framework for Ocean Observing: Evolution of the Global Ocean Observing System
Bibliographic record
Abstract
The Global Ocean Observing System (GOOS) and its partners have worked together over the past decade to break down barriers between open-ocean and coastal observing, between scientific disciplines, and between operational and research institutions. Here we discuss some GOOS successes and challenges from the past decade, and present ideas for moving forward, including highlights of the GOOS 2030 Strategy published in 2019. The OceanObs'09 meeting in Venice in 2009 resulted in a remarkable consensus on the need for a common set of guidelines for the global ocean observing community. Work following the meeting led to development of the Framework for Ocean Observing (FOO) published in 2012 and adopted by GOOS as a foundational document that same year. The FOO provides guidelines for the setting of requirements, assessing technology readiness, and assessing the usefulness of data and products for users. Here we evaluate successes and challenges in FOO implementation and consider ways to ensure broader use of the FOO principles. The proliferation of ocean observing activities around the world is extremely diverse and not managed, or even overseen by, any one entity. The lack of coherent governance has resulted in duplication and varying degrees of clarity, responsibility, coordination and data sharing. GOOS has had considerable success over the past decade in encouraging voluntary collaboration across much of this broad community, including increased use of the FOO guidelines and partly effective governance, but much remains to be done. Here we outline and discuss several approaches for GOOS to deliver more effective governance to achieve our collective vision of fully meeting society's needs. What would a more effective and well-structured governance arrangement look like? Can the existing system be modified? Do we need to rebuild it from scratch? We consider the case for evolution versus revolution. Community-wide consideration of these governance issues will be timely and important before, during and following the OceanObs'19 meeting in September 2019.
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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 | 0.062 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.021 | 0.044 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".