7.3.0 Panel 7.3.0: SysML Early Applications and Lessons Learned
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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