Implémentation à l'aide de BPEL de trois processus d'agrégation de composants, dirigée par les modèles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it