World Distribution Protocol for Support of Scalable General Purpose Distributed Virtual Environments
Bibliographic record
Abstract
General purpose distributed virtual environments (DVEs) require more rigorous architecture design than those of case specific environments. Several world distribution methods have been developed for specific usage in various vehicletraining simulators. The VPMS project at the University of Windsor is concerned, in part, with investigating strategies for design and implementation of a system, which adapts existing world distribution protocols for general purpose DVEs. In context of these protocols, the world is partitioned based on physical proximity in the virtual world; databases for a given part of the world are established and maintained locally. One obstacle that needs to be overcome is the size difference of the area of influence (AOI) of a given entity. Since AOI size varies for different entity types, as occurs in aircraft training involving group based entities, existing schemes which fix AOI size are inappropriate to deal with the issues and requirements of modem, distributed modelling, visualization and collaboration contexts and, therefore, new schemes must be developed. Some approaches to this problem are described and design considerations for a general-purpose system are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".