Architecting blockchain network simulators: a model-driven perspective
Bibliographic record
Abstract
Blockchain networks have been suggested to have the potential to support some of the most critical functions of modern societies. When used in such capacities, failures of blockchain networks imply catastrophes that extend beyond individuals, organizations and countries. As such, before considered for wide adoption, blockchain network protocols and technologies must undergo the highest standards of analytical and empirical validation subject to key security, reliability and performance qualities. When performing empirical evaluation, however, the sheer size of open-access blockchain networks in their envisioned scale rules out the possibility of exact reproduction and validation in a lab environment. Rather, abstract working models - simulators - of proposed technologies need to be considered. To have value as research instruments, such simulators need to be widely validated for their accuracy by the research community, and also be highly transparent and reusable for allowing quick implementation and comparison of design ideas. We claim that established software engineering paradigms, namely model-driven development and software product lines can help address this need. We outline our own effort to develop a domain meta-model and object-oriented framework for efficient and reliable derivation of specialized blockchain network simulators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".