Bibliographic record
Abstract
Abstract The IEEE 1516 Standard ‘High Level Architecture (HLA)’ and its implementation ‘Run‐Time Infra‐structure (RTI)’ defines a general‐purpose network communication mechanism for Distributed Interactive Simulation (DIS). However, it does not address real‐time requirements of DIS. Current operating system technologies can provide real‐time processing through some real‐time operating systems (RTOSs) and the Internet is also moving to an age of Quality of Service (QoS), providing delay and jitter bounded services. With the availability of RTOSs and IP QoS, it is possible for HLA to be extended to take advantage of these technologies in order to construct an architecture for Real‐Time DIS (RT‐DIS). This extension will be a critical aspect of applications in virtual medicine, distributed virtual environments, weapon simulation, aerospace simulation and others. This paper outlines the current real‐time technology with respect to operating systems and at the network infrastructure level. After summarizing the requirements and our experiences with RT‐DIS, we present a proposal for HLA real‐time extension and architecture for real‐time RTI. Similar to the growth of real‐time CORBA (Common Object Request Broker) after the mature based CORBA standard suite, Real‐Time HLA is a natural extension following the standardization of HLA into IEEE 1516 in September 2000. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".