Selecting a HLA Run-Time Infrastructure: Overview of Critical Issues Affecting the Decision Process for
Bibliographic record
Abstract
© Sa majesté la reine, représentée par le ministre de la Défense nationale, 2004 The High Level Architecture (HLA) is a distributed simulation architecture designed to facilitate interoperability and promote software reuse within the modeling & simulation (M&S) community. In HLA, the unit of software reuse is the federate. Federates communicate via a distributed middleware called the Run-Time Infrastructure (RTI). The HLA specifies the interface between each federate and the RTI but does not specify how the RTI is implemented. As such, several RTI implementations exist. All federates must choose a single RTI implementation in order to interoperate at run-time. This paper discusses the technical, political and economic considerations one must weigh when selecting a HLA RTI implementation. Résumé L’architecture de haut niveau (AHN) est une architecture de simulation répartie conçue pour faciliter l’interopérabilité et promouvoir la réutilisation des logiciels au sein de la collectivité de modélisation et de simulation (M&S). Dans une AHN, l’unité de réutilisation des logiciels est le fédéré. Les fédérés communiquent au moyen d’un intergiciel réparti appelé infrastructure valorisée à l’exécution (IVA). L’AHN précise l’interface entre chaque fédéré et l’IVA, mais n’indique pas comment l’IVA est mise en application. En fait, il y a plusieurs versions d’IVA. Chaque fédéré doit en choisir une afin que l’exécution soit interopérable. Le présent document traite des facteurs techniques, politiques et économiques à considérer lorsqu’on choisit une version d’IVA pour une AHN. DRDC Ottawa TM 2004-111 i CLASSIFICATION / DESIGNATION
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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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".