Reconfigurable Semi-Analytic Sensitivity Methods and MDO Architectures within the piMDO Framework
Bibliographic record
Abstract
framework is presented. Using the inherent advantages of MDO’s object oriented and highly exible structure, several semi-analytic sensitivity methods are implemented for the MDO architectures within the framework. Their generalized nature allows the application of these powerful and ecient methods to any problem dened within MDO, without modication to the existing structure of the problem. Further, exploiting information gathered by these methods, a new \meta MDO architecture is proposed which dynamically recongures the problem to speed the optimization while maintaining the delity of the original analysis. This hybrid approach uses the existing architectures in MDO encapsulated within each other to reduce the dimensionality of coupling between disciplines. Again, due to the object oriented nature of MDO, no modications are required to the problem statement or the MDO architectures within the framework. Initial results suggest that both these additions produce valuable performance gains while maintaining the general exibility and simplicity characteristic of MDO.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".