Ocean Integration: The Needs and Challenges of Effective Coordination Within the Ocean Observing System
Bibliographic record
Abstract
Understanding and sustainably managing complex environments such as marine ecosystems benefits from an integrated approach to ensure that information about all relevant components and their interactions at multiple and nested spatiotemporal scales are considered. This information is based on a wide range of ocean observations using different systems and approaches. An integrated approach thus requires effective collaboration between areas of expertise in order to improve coordination at each step of the ocean observing value chain, from the design and deployment of multi-platform observations to their analysis and the delivery of products, sometimes through data assimilation in numerical models. Despite significant advances over the last two decades in more cooperation across the ocean observing activities, this integrated approach has not yet been fully realized. The ocean observing system still suffers from organizational silos due to independent and often disconnected initiatives, the strong and sometimes destructive competition across disciplines and among scientists, and the absence of a well-established overall governance framework. Here, we address the need for enhanced organizational integration among all the actors of ocean observing, focusing on the occidental systems. We advocate for a major evolution in the way we collaborate, calling for transformative scientific, cultural, behavioral, and management changes. This is timely because we now have the scientific and technical capabilities as well as urgent societal and political drivers. The ambition of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030) and the various efforts to grow a sustainable ocean economy and effective ocean protection efforts all require a more integrated approach to ocean observing. After analyzing the barriers that currently prevent this full integration within the occidental systems, we suggest nine approaches for breaking down the silos and promoting better coordination and sharing. These recommendations are related to the organizational framework, the ocean science culture, the system of recognition and rewards, the data management system, the ocean governance structure, and the ocean observing drivers and funding. These reflections are intended to provide food for thought for further dialogue between all parties involved and trigger concrete actions to foster a real transformational change in ocean observing.
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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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.010 |
| 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".