Bibliographic record
Abstract
Abstract. In Model Driven Engineering (MDE), models and mappings play a key role in system design. However, in practice, models and map-pings do not exist in isolation, but are combined to form systems of interrelated models. We call the trace of operations, such as model trans-formations or model merges, between an initial configuration of a system of interrelated models to a final one, a workflow. Current approaches for using workflows in MDE exist, but are generally informal and do not properly address traceability and verification. In this work, we propose a structured method for defining workflows for model management, which automatically ensures traceability and inherently enables verification. This approach also sets the stage for defining a declarative workflow lan-guage, which we believe can aid in validation. Through this framework, comparison and optimization of workflows is possible, as they are repre-sented as algebraic terms in a mathematically defined language. Finally, the framework gives rise to multiple levels of abstraction, making it flex-ible enough to be used at different stages of the system design, while enabling better workflow readability and maintainability. 1
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".