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Record W4408816552 · doi:10.5194/oos2025-1177

Ocean Best Practices: Advancing Interoperability and Equity for a Sustainable Ocean

2025· preprint· en· W4408816552 on OpenAlexaff
Cristian Muñoz, Rebecca Zitoun, Jay Pearlman, R. Garello, George Petihakis, Cora Hoerstmann, Talen Rimmer, Pauline Simpson, Patricia Cabrera

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInteroperabilityEquity (law)BusinessOceanographyComputer sciencePolitical scienceGeologyWorld Wide Web

Abstract

fetched live from OpenAlex

Achieving the UN Ocean Decade's objectives necessitates coordinated, harmonized, and interoperable ocean observations and analyses across disciplines and scales. Central to this is adopting standardized methodologies—including guidelines, policies, protocols, and specifications—that underpin collaboration, data interoperability, and sustainable ocean management.Best practices in ocean observing offer significant benefits; shared, well-documented methods support global and regional interoperability, capacity development and innovation across the ocean science value chain—from requirements setting, through observations and data management, to end-user applications and societal impacts.The Ocean Best Practices System (OBPS) is a transformative initiative of the Intergovernmental Oceanographic Commission (UNESCO-IOC) facilitating the creation, archiving, discovery, and dissemination of methods, best practices, and standards. OBPS addresses the need for reliable, interoperable ocean data use, supporting a comprehensive global observing network from the deep ocean to the coast. It fosters knowledge transfer and capacity development, particularly for Small Island Developing States (SIDS) and Least Developed Countries (LDCs), addressing barriers to diversity and equity.By focusing on methods standardization—a cornerstone of ocean activities—OBPS promotes inclusivity and collaboration across the ocean community. It interconnects all Ocean Decade Programmes , enabling the creation, sharing, and dissemination of accessible methodologies and best practices. This ensures greater participation of diverse stakeholders while supporting sustainable ocean management and innovation.OBPS is carrying out different activities to fill existing gaps in creating, sharing, and interconnecting ocean observing knowledge and infrastructure globally:Training and Capacity Development among individuals and countries to develop and apply ocean best practices. Development of a Maturity Model for Practices [1] based on a five-level model differentiates between 'good' and 'best' practices, guiding practitioners toward continuous improvement. Establishing a Federated System for Methodology Sharing through distributed systems to allow organizations to maintain autonomy over assets, enhancing metadata sharing across repositories. This initiative enhances cross-indexing and discovery across repositories under the IOC OBPS and the Ocean Data and Information System (ODIS), in collaboration with the Food and Agriculture Organization (FAO) and the International Council for the Exploration of the Sea (ICES). Linking Datasets and Methodologies for Traceability with the European Marine Observation and Data Network (EMODnet) that links datasets and methodologies in OBPS, aiming to expand to global repositories like the Ocean Biodiversity Information System (OBIS). Supporting the contributions of the OBPS OceanPractices Ocean Decade Programme to Ocean Decade vision and programmes OBPS plays a crucial role in enhancing the knowledge generation process and ensuring that best practices are widely accessible and adopted to ultimately support informed decision-making processes in ocean management. By linking maturity-assessed practices through a federated system of methodologies, OBPS fosters a cultural shift that places ocean knowledge ahead of policy.[1] Mantovani C, et al (2024) An ocean practices maturity model: from good to best practices. Front. Mar. Sci. 11:1415374. doi: 10.3389/fmars.2024.1415374

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.072
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.096
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.008
Science and technology studies0.0060.017
Scholarly communication0.0280.036
Open science0.0050.036
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0130.006

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.

Opus teacher head0.031
GPT teacher head0.325
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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