A Robust ASE Correlation and Analysis Method
Bibliographic record
Abstract
A robust method for Structural Coupling Test (SCT) correlation and subsequent Aeroservoelastic (ASE) analysis is presented. The method provides a means with a limited set of SCT test conditions to develop a correlation model that can be applied to the analysis of many other configuration/condition variations such as fuel states, flight conditions, internal store and external store carriage, and for modernization. The method makes use of a set of normal modes and other generalized coordinates; all used as ‘primitive’ modes during both correlation and analysis. This uses a set of correlation variables or coefficients whose values are established during the correlation process and then used throughout all the subsequent analysis. Several correlation solution techniques are discussed but the application presented uses an optimization process. As is evidenced by the data presented, the method resulted in very good correlation. The method also resulted in a significant reduction in correlation and post-correlation analysis times. The development of this method was driven by a SCT being conducted on a high-serial-number F-22 to determine the effect of changes that had occurred in the aircraft since EMD.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".