Adapting Workflow Management Systems to BFT Blockchains -- The YAWL\n Example
Bibliographic record
Abstract
Blockchain technology provides an auditable and tamper-proof distributed\nstorage infrastructure for information records. This can be leveraged to\nsupport distributed workflow management. Compared to proof-of-work consensus,\npopularized by Bitcoin and Ethereum, blockchains based on BFT (byzantine fault\ntolerance) ordering consensus trade scalability for immediacy and finality of\nconsensus. This makes them easier to use as distribution infrastructure, as\napplications need not be adapted to deal with eventual consistency and delayed\nconsensus of proof-of-work blockchains. Hence, applications such as workflow\nengines can be easily ported to such a blockchain infrastructure to take\nadvantage of their decentralized integrity assurance and information\ndistribution model. In this paper we describe how the YAWL workflow engine can\nbe used on a BFT based blockchain infrastructure to enable collaborative\nworkflows across different organizations.\n
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".