Model-Driven Integration of Domain Knowledge into Machine Learning Workflows: A Case for Multidisciplinary System Design
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
Complex multidisciplinary energy systems, such as gas turbines, and power systems involve several interrelated subsystems, each designed by special engineering teams with dedicated domain expertise. The rapid adoption of machine learning (ML) in the design and manufacturing of such systems introduces several software engineering challenges. An important challenge is how to incorporate engineering knowledge from domain experts into a machine learning workflow in a systematic and (semi-)automated way. This paper presents a vision towards a model-driven approach to address this challenge by capturing domain knowledge using knowledge graphs. Using gas turbines as a use case, we propose a high-level architecture that supports the iterative evaluation of ML models through automated performance reporting enriched with domain insights.
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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 | 0.001 |
| 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