Bibliographic record
Abstract
In System Engineering, many systems encompass widely different domains of expertise; there are several challenges in relating these domains due to their heterogeneity and complexity. Although, literature provides many techniques to model traceability among heterogeneous domains, existing solutions are either tailored to specific domains (e.g., Ecore modeling languages), or not complete enough (e.g., lack support to specify traceability link semantics). This paper proposes a generic traceability model that is not domain specific; it provides a solution for modeling traceability links among heterogeneous models, that is, systems for which traceability links need to be established between artifacts in widely different modeling languages (e.g., UML, block diagrams, informal documents). Our solution tackles the drawbacks of existing solutions, and incorporates some of their ideas in an attempt to be as complete as possible. We argue that our solution is extensible in the sense that it can adapt to new modeling languages, new ways of characterizing traceability information for instance, without the need to change the model itself.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".