Implementing Standards Suite for Ocean Digital Twins in Iliad
Bibliographic record
Abstract
Data discovery, processing and access APIs play an important role in the integration and harmonization of Digital Twins' implementations. Within the Iliad project's System of Systems (SoS) federated approach to the distributed platform architecture for digital twins operating oceanographic data, APIs have been applied. By enabling interoperability between diverse sources—legacy sensors, citizen science projects, third-party data hubs, and models—these APIs ensure standardized access to distributed datasets without requiring a centralized repository. A key focus of this paper is the implementation of flexible, open API architectures that align metadata, standardize access protocols, and facilitate data discovery across the system of systems (SoS). The adoption of cloud-native formats, rich web services, and streaming mechanisms enhances accessibility while supporting both existing and emerging technologies. The paper explores efforts around standards development organizations to refine API frameworks into knowledge services, ensuring they remain adaptable to evolving requirements. These advancements are instrumental in enabling high-resolution modeling, large-scale simulations, and leveraging potential of the AI applications within the environmental Digital Twins ecosystem.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".