A User-Flow approach for multi-user applications with DMS (Distributed Modules System)
Bibliographic record
Abstract
We have developped tools that bring to non-programmer people the power to design multi-user applications over internet. Multi-user application is not only 3d virtual world, it is more generally application in which people can interact with or through the same "object". Because they are addressing non-programmer people, these tools have to hide programming problems as well as distribution problems. This paper describes the DMS architecture on which both tools and applications are based. VIRTUAL REALITY AND PROTOTYPING June 1999, Laval (France) 1 Introduction 1.1 Starting point In this paper, multi-user application stands for application in which users can interact together over the network. This interaction is organized around a server which is at least the meeting point of users, but which usually runs some parts of the application. Multiuser applications is most probably the next step in internet conquest, after email, forums, broadcasting informations (Html), videoconferencin...
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".