Implémentation à l'aide de BPEL de trois processus d'agrégation de composants, dirigée par les modèles
Bibliographic record
Abstract
RESUME. Plusieurs organisations qui œuvrent dans le domaine d’apprentissage a distance utilisent le composant logiciel comme unite de base pour construire leur systeme. Ils ne developpent presque plus de nouveaux composants, mais ils les reutilisent et appliquent des reingenieries pour des fins d’adaptation aux nouveaux contextes. Ceci prouve que le developpement logiciel par agregation des composants est un sujet d’interet. Cette branche du genie logiciel constitue un des axes fondamentaux du projet canadien LORNET (Learning Object Repositories’ NETworsk). Cet article donne suite a des travaux publies l’an dernier, proposant principalement d’adjoindre aux composants logiciels certains types de metadonnees que nous avons intitule SOCOM (SOftware COmponent Metadata). Nous avons defini trois types d’agregations avec des exemples concrets. Dans le present article, nous rappelons brievement ces metadonnees et les categories d’agregation existantes et proposees et nous utilisons un langage d’execution de processus metier intitule BPEL (Business Process Execution Language) pour implementer des categories d’agregation tels que : Collection, Coordination et Fusion. ABSTRACT. Many organizations research on develop eLearning environments based on software components as their system’s base construct. They don’t develop new components, but they reuse existing ones. They apply software engineering concepts such as reengineering, reverse engineering and software components reuse. System development based on software components is an important issue also in LORNET project (Learning Object Repositories’ NETworks). This paper extends some previous work. We remind briefly our SOCOM (SOftware COmponent Metadata, metadata structure that characterizes software components) and we explain shortly our aggregation classification based on three attributes from SOCOM. Afterwards we use BPEL as a business process execution language to help us to implement our three designed aggregation’s categories: aggregation by collection, by coordination and by fusion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".