Launching GEBCO’s Vision to bring knowledge about our planet’s seabed to everyone
Bibliographic record
Abstract
GEBCO – the General Bathymetric Chart of the Ocean programme – was funded in 1903 by Prince Albert I of Monaco to deliver “the most authoritative, publicly available bathymetry of the world’s oceans” by providing bathymetry data to the world. Bathymetry is necessary for the mapping and charting of underwater features and the topography of the seabed.Today, GEBCO is an internationally recognised and well-respected programme that operates under the joint auspices of the International Hydrographic Organization (IHO) and the Intergovernmental Oceanographic Commission (IOC) of the United Nations Educational, Scientific and Cultural Organization (UNESCO). In 2024, to enable GEBCO to fulfil its expansive and ambitious vision, GEBCO updated its strategy.GEBCO's new vision is to bring knowledge about our planet’s seabed to everyone.and GEBCO's adopted new mission is to produce free, open and complete seabed datasets of the world’s oceans by enabling and inspiring seabed mapping efforts through international capacity development, education, and collaboration.The Strategy broadens GEBCO’s focus to encompass seabed data and datasets, including bathymetry and its derivatives, positioning the programme firmly in the twenty-first-century mainstream of ocean science. It will focus its efforts on providing data that support information and knowledge on the shape of the seabed and help support dedicated governance that strives to increase GEBCO’s visibility and relevance in a world increasingly more aware of the importance of the ocean. GEBCO will promote seabed mapping activities focused on the creation of a definitive set of seabed data of the world ocean spearheaded by the Nippon Foundation GEBCO Seabed 2030 flagship project. It will provide GEBCO with clear direction within the complex structure and relationships between parent organisations, subcommittees and subordinate projects.Seabed data are essential to better understand the geophysical processes that control the dynamics of the seafloor and inform the oceanographic processes that control ocean circulation. These elements are necessary to better understand, protect, conserve and sustainably develop the ocean, the seafloor and the ecosystems and resources it supports. Bathymetry and seabed data are foundational to ocean sciences and required to achieve all six Ocean Decade Outcomes.GEBCO’s outcomes and objectives are organised through five pillars critical to achieving its Vision and Mission: (1) Delivering open and fit-for-purpose seabed data,; (2) Supporting, promoting and using innovative solutions to continuously improve the GEBCO data value chain; (3) Establishing global infrastructure to develop capacity; (4) Engaging communities and partners to best deliver GEBCO’s mission; and (5) Gaining support for our mission through robust processes that influence decision-making.After 120 years of activity, GEBCO more than ever must think about the future it wants for the ocean for the coming generations. GEBCO’s future activities will continue to aim at improving humanities knowledge of the ocean through striving to increase free and easy access to seabed datasets and related knowledge. GEBCO will continue to contribute to the overarching Ocean Decade outcomes, whereby striving for Oceans will be clean, healthy and resilient, productive, predictable, safe, accessible as well as inspiring and engaging.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".