5G-IANA - D2.1 Specifications of the 5G-IANA architecture
Bibliographic record
Abstract
This deliverable has the objective to provide the outcomes of the activities performed in Work Package (WP) 2 "Specifications". The activities included the design of the 5G-IANA Automotive Open Experimentation Platform (AOEP) and the requirements specification of each architecture layer. The specified 5G-IANA architecture capitalizes on the 5G prospect of being a unified multi-service platform by orchestrating Vertical Services based on virtualized network slices and coordination of distributed edge-to-cloud deployment. The 5G-IANA AOEP aims to provide an open and flexible experimentation platform to third-parties developers (e.g., SMEs) that want to develop new 5G-based services devoted to the Automotive vertical. The availability of an easy-to-use experimentation environment can facilitate the launch of new services creating new market opportunities. Moreover, 5G-IANA will actively address the configuration of the 5G network (e.g., network slicing, edge resources, etc.) with the objective of supporting in the best way the requirements of the new services. In this way, it will be also possible to verify if the current 5G implementation can adequately satisfy the highly demanding performance requirements of Automotive services.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.040 |
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".