COASTS: A Digital Twin for Coastal Resilience and Blue Carbon Ecosystems
Bibliographic record
Abstract
Coastal zones around the world are vital for our environment, economy, and communities, highlighting the urgent need to use innovative technologies to address significant environmental challenges. Digital twins, a leading technology in environmental modeling and simulation, offer great potential for improving our understanding and management of these crucial areas, especially blue carbon ecosystems, by using advanced hydrodynamic and morphodynamic modeling.Coastal areas, which embody a diverse range of ecosystems such as mangroves, seagrasses, and salt marshes, generally referred to as blue carbon ecosystems, provide a wealth of ecosystem services and support rich biodiversity. These ecosystems are just as important for the environment as they are for the economy, supporting essential industries and providing livelihoods globally. However, climate change and harmful human activities pose serious risks to coastal zones, making it crucial to develop informed management strategies.Blue carbon habitats are vital to carbon sequestration efforts, playing a critical role in climate regulation. Despite this, they face increasing threats from sea-level rise, pollution, overfishing, and habitat destruction, with alarming loss rates observed in mangroves and seagrasses, which results in the release of stored carbon and degradation of key ecosystem services.To address these challenges, the COASTS (Coastal Observation Advances leveraging Space Technology Services) international initiative, proposes an innovative solution, integrating advanced Earth Observation data with state-of-the-art modeling and monitoring techniques. This integrated approach enables the creation of comprehensive digital twins, simulating coastal processes and blue carbon ecosystem dynamics. By leveraging the capabilities of the Copernicus Marine Service, the initiative combines advanced technological resources with actionable environmental insights.The COASTS project aims to develop a digital twin for coastal ecosystem resilience and blue carbon optimization, enabling stakeholders to develop effective management strategies specifically designed for these habitats. This effort will evaluate both the erosion protection abilities of coastal ecosystems and their ecosystem services, such as carbon sequestration ability. Through the integration of high-resolution EO data, the project will map spatial-temporal dynamics within coastal zones, establishing critical baseline information for conservation and restoration efforts.The project's pilot sites in Germany, Jersey, and the Maldives will provide testbeds for the system, facilitating its eventual application and scalability to other regions. The collaboration with stakeholders enriches the project's collaborative foundation, ensuring it meets the needs and acceptance of end users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".