INSTAR EU-Canada Digital Partnership Week on ICT Standardisation - Master slides Day 1: Cloud-Edge-IoT (CEI): Building cross-sectoral standardisation interfaces
Bibliographic record
Abstract
Cloud-Edge-IoT standardisation is a key enabler of digital innovation across various sectors worldwide, including mobility, energy and manufacturing. It is also a critical component of the twin Green and Digital transition. In this session, INSTAR will bring forward the outcomes of the workshop on “Cross-Domain Standardisation and Architecture for IoT and Edge Computing” with South Korea’s TTA, and onto the table with the Canadian counterparts. The aim is to align on key strategic and technical aspects, to facilitate greater interoperability across the Cloud to Edge Continuum and promote industrial competitiveness across the two jurisdictions through trade.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.134 | 0.052 |
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".