A Blockchain-Driven Electronic Contract Management System for Commodity Procurement in Electronic Power Industry
Bibliographic record
Abstract
In the era of paper-based contract, a lot of time, human labor and expenses are required to handle the process of contract drafting, contract signing, contract execution, and payment settlement. The emerging of electronic contract enhances the tedious signing process of paper-based contract and improves the efficiency of contract management. However, due to the centralized system architecture and the database-based storage schema, the stored contract data is at high risk of information leaking, data tampering and hacker attacks. In this study, we introduce Blockchain technology to the contract management, and develop a process-oriented contract management system (BEcontractor) for a Hangzhou-located power grid enterprise X, aiming to solve a series of security issues existing in the traditional electronic contract system. By deploying BEcontractor, procurement activities could be resumed online among X and its nation-widely commodity suppliers during COVID-19 epidemic. Up to September 2020, 6336 electronic contracts have been signed, with an accumulated amount of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\yen ~6.5$ </tex-math></inline-formula> billion. It is showed that the cost for accomplishing the contract signing process was significantly reduced, and the payment period was shortened from three months to around one month.
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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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".