Cerulean Information Factory: A New European Space Agency-Funded Project to Develop Decision-Support Tools for the North Atlantic and Arctic Oceans
Bibliographic record
Abstract
The The European Space Agency (ESA), responding to the European Green Deal-Europe's commitment to climate neutrality by 2050-has launched the Green Transition Information Factory (GTIF) program. Its goal is to use satellite and other data to create tools for decision-makers to support the transition to a greener economy. “The ESA Green Transition Information Factory (GTIF) allows users to interactively discover the underlying opportunities and complexities of transitioning to carbon neutrality by 2050 using the power of Earth Observation, cloud-computing and cutting-edge analytics.” - ESA The Green Economy is “one that results in improved human well-being and social equity, while significantly reducing environmental risks and ecological scarcities” (United Nations). The Blue Economy refers to “sustainable use of ocean resources for economic growth, improved livelihoods and jobs, and ocean ecosystem health.” (World Bank). Since the oceans are part of all natural cycles and, directly or indirectly, involved in all economic sectors, there cannot be a Green Transition without the blue component. ESA is sponsoring a GTIF that addresses a green transition for the blue economy named the Cerulean Information Factory (CIF) after the blue-green colour and reflecting the need to connect the Blue and Green Economies in order to have a successful Green Transition. The focus of the project, launched in Spring of 2024, will be on the North Atlantic and Arctic oceans between Canada and Europe. The initial capabilities will focus on three domains: 1.offshore Renewable Energy: Integrating multiple data sources to provide user-definable metrics for offshore renewable energy potential and risk for offshore renewable energy, including solar, tidal, wave, wind and current energy. Analytical capabilities will include: •energy potential of sites; •accessibility of sites, including water depth, and distance to shore and energy markets; •vulnerability of sites to adverse environmental conditions, including sea ice, icebergs, and extreme waves and winds; and, •forecasts for winds, sea ice, waves and currents. 2.Ship Carbon Intensity Minimisation and Arctic Accessibility: Providing a route optimization tool for vessels in ice that minimises fuel consumption, ship emissions, and travel time, while maintaining ship safety in sea ice and icebergs. This capability will provide a tool to support both: •Ship operators with voyage planning, and, •Policymakers in evaluating the impact of the International Maritime Organization's Carbon Intensity Indicator (CII) regulation and future changes in the accessibility of Arctic regions due to climate change. 3.Aquaculture: Providing indicators of ocean health and potential risks for aquaculture sites by enabling end-users to filter and combine ocean and meteorological observations and forecasts. This capability will support: •Assessment of aquaculture site suitability based on historical water quality and presence of microorganisms, such as temperatures and nutrients, and, •Early warning systems, such as harmful algal blooms, pollution and eutrophication. Throughout the project, the CIF team will be engaging with end users to identify requirements and co-develop the tools that decision-makers in industry, government and civil society need; transforming ocean data into information for decision making. The project team is made up of a consortium of organisations with Earth Observation expertise from both sides of the Atlantic Ocean, including Polar View, EOX, Danish Meteorological Institute, C-CORE, and the National Research Council Canada. At OCEANS 2024, we will demonstrate the initial capabilities of the CIF.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".