Exploration of a Multi-User Collaborative Assembly Environment on the Internet: A Case Study
Bibliographic record
Abstract
Abstract Real-time collaboration systems, in which participants share product data and applications in real time, have been a subject of interest for many years. Nowadays, a rapid development of Internet-based technologies with steadily increasing easiness in accessing any kind of information through the World Wide Web (WWW) would offer the possibility of developing a real-time collaborative system over the Internet. Two strategies are required to create such a system. One strategy is finding effective methods for communicating and sharing distributed product information, especially those related to design and manufacturing. Another strategy is developing Web-based approaches that support real-time sharing of platform-independent applications. In this paper, a concept for a multi-user collaborative assembly environment on the Internet is presented. The Client/Server structure of the environment, and the four main functional modules including: 1) integration and sharing of distributed product data through a STEP server; 2) session management including team management, user management and access control; 3) sharing of multimedia data (e.g. text, audio and video); 4) 3D collaborative assembly, are described. Finally, a scenario has been designed to demonstrate the effectiveness of the environment to support distributed collaborative assembly design.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".