AP236-XML: A Framework for Integration and Harmonization of STEP Application Protocols
Bibliographic record
Abstract
Today, organizations have to deal with the integration of applications across company borders, and must support collaboration of business processes between organizations in networked environments that are seamless, flexible and interoperable. This characteristic of horizontality, where one application embraces more than one domain of activity, is frequently found and raises the need for integration and cooperation between multiple standard application protocols and business objects. However, a standard for data representation cannot cove the whole range of activities one application needs to handle. This paper contributes by proposing a framework that supports interoperability in distributed heterogeneous networked environments, assisting in the integration of reference models described following dissimilar methodologies. This framework assists in the automatic mapping between ISO10303 STEP, UML and XML models. The proposed work results from research developed and validated in the scope of the IMS SMART-fm project (www.smart-fm.funstep.org, www.ims.org), involving partners from USA, Europe, Canada and Australia, using emerging approaches for modeling and technology.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".