7.3.0 Panel 7.3.0: SysML Early Applications and Lessons Learned
Bibliographic record
Abstract
Abstract SysML is a general purpose systems modeling language that was adopted by the OMG in May 2006 (announced in early July). SysML is considered a key enabler to transition to model based systems engineering. The panel members will present what they have learned from their early experiences in implementing SysML from their diverse viewpoints, including end‐user, tool vendor and academic perspectives. Topics will include: End user perspective: Highlight industry experience on projects including What works, What methodologies are being employed, What is difficult, What is the response from the various stakeholders (customer, PM, software and hardware developers, testers) and What are suggested areas of improvement? Vendor perspective: Highlight tool vendor experiences including What SysML features are most requested, What has been difficult to implement, How well does SysML integrate with UML, What are suggested areas of improvement? Academia perspective: Highlight Academia experiences with SysML including Where does SysML fit in the curriculum and in research, What is difficult to teach, What is the response from students and faculty, What do you feel they are learning, and What are suggested areas of improvement (both from an educational perspective and a modeling language research perspective)? The moderator will also stimulate questions that cross the various viewpoints.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".