RI-SCALE_D3.1 – AI Systems and Models Specification and Roadmap
Bibliographic record
Abstract
The Data Exploitation Platform (DEP) enables Research Infrastructures (RIs) to scale their AI applications across large-scale computing infrastructure, such as on cloud and High Performance Computing (HPC) systems, and enables scientists to train and/or run AI models at scale with RI scientific data. Work Package (WP) 3 plays a central role in enabling these capabilities by providing the technical solutions needed to integrate AI functionalities in the DEP. This deliverable outlines the technical specifications of the AI solutions proposed in WP3 for the DEP, detailing their main features, planned developments and integrations. The document also presents user stories that illustrate scenarios of accessing the DEP for different kinds of users. These stories highlight the DEP access mechanisms and the role of the software solutions in the DEP. The AI applications and their compute and data requirements are also defined in this document. These requirements and the user stories guide the modular architecture of WP3, which enables flexible and customizable definition of workflows for DEP users. Finally, the document proposes the creation of testbeds, which form the methodological basis for realizing the technical implementations in the DEP.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.029 |
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".