Harmonising technologies in conceptual models representation
Bibliographic record
Abstract
The lack of interoperability among systems, applications, and services has been hindering collaboration between organisations raising the need for integration and cooperation between multiple standard business objects and application protocols. ISO 10303-STEP has been acknowledged by most of the industrial companies as the most important family of standards for the integration and exchange of product data under the manufacturing domain. However, STEP standards use technologies unfamiliar to most application developers. This paper presents a platform for the harmonisation of technologies used in the representation and implementation of conceptual models, showing how different standard models and technologies can be unified for industrial benefits, enabling organisations to take the most from all of them. This work results from the research developed and validated in the scope of international research projects involving partners from USA, Europe, Canada, and Australia, under the scope of the IMS SMART-fm, ATHENA IP, and INTEROP NoE European research projects.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".