A Decentralized Web 3.0 Platform for Manufacturing Customized Products
Bibliographic record
Abstract
Web 3.0 promises a more decentralized and valuable Internet by allowing users to truly own and control their data. This is in contrast to Web 2.0, where users and their generated data and traffic have been centralized by several major Internet platforms. In this article, we investigate the design of Web 3.0 platforms through a case study. Specifically, we propose a platform targeting the long-tail market comprising customized products. To handle the decentralized user demands, we realize the concept of virtual enterprise (VE) on the platform, such that different users can cooperate through running specific VEs with predefined workflows, thus greatly reducing the transaction costs. Furthermore, we develop the micro-blockchain protocol, allowing users to store consistent copies of transaction data on their own devices. Unlike existing coin-based blockchains, micro-blockchain does not require global consensus, hence it can naturally scale with more concurrent transactions. Moreover, a data-driven trust model is utilized, so that users can evaluate their trust relationship with others solely based on the data of their past activities, instead of requiring the help of a centralized trust authority, which is usually acted by the platform. At last, we point out the key issues on designing general Web 3.0 applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".