Bibliographic record
Abstract
Abstract The OMG Systems Modeling Language (OMG SysML™) is a general‐purpose, graphical, modeling language for specifying, analyzing, designing, and verifying complex systems that may include hardware, software, information, personnel, procedures, and facilities. In particular, it provides graphical representations with a semantic foundation for modeling system requirements, behavior, structure, and parametric equations that can integrate with a broad range of engineering analysis. SysML represents a subset of UML 2.0 with extensions to satisfy the requirements of the UML™ for Systems Engineering RFP. The OMG SysML™ Specification was adopted in May 2006. For more information on SysML, including articles, tool vendor and related links, and the specification, go to http://www.omgsysml.org/ . This tutorial provides an introduction to how SysML can address your systems engineering needs, including: background and motivation, an overview of the SysML diagram types and language concepts, a demonstration of how the language can be used throughout a Model‐based Systems Engineering (MBSE) process. A class exercise is included to help solidify the student's understanding of the language. The course will also include a brief introduction to a typical SysML tool. Attendees should gain appreciation of the value of model‐based Systems Engineering versus legacy, document‐centric methods; awareness of the graphical notation; and a high level understanding of when and how a Systems Engineer can exploit the various diagrams and models as part of their systems engineering process.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".