Bibliographic record
Abstract
It is with great pleasure to welcome you to 3rd Conference on Blockchain Research & Applications forInnovative Networks and Services, that will be held on September 27-30, 2021 in Paris, France, one of the most cosmopolitan capitals in the world.Blockchain and Distributed Ledger Technologies (DLT) make global headlines daily electrifying the world by disrupting the techno-socio-economic fabric of our society including the coordination among autonomous resources.Other areas include finance and payments (e.g., Facebook Libra), but also networks (e.g., power grids or telecom networks), computing (e.g., brokering of edge resources), IoT (e.g., supply chain or industry 4.0) or service platforms (e.g., identity management).BRAINS conference series is dedicated to these new advances that could make the world of networks and services more secure, while enabling new distributed business models.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.230 | 0.236 |
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".