Parameter design considering the impact of design changes on downstream processes based upon the Taguchi method
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
Abstract This research introduces a new systematic approach for parameter design considering the impact of design changes on downstream processes. In this approach, design parameters with potential changes are modelled as noise parameters, while design parameters without potential changes are described as controllable parameters. The Taguchi method is employed to identify the robust design whose downstream process is the least sensitive to design parameter value changes. Since design parameter changes are usually associated with probabilities, the Taguchi method is modified in this research considering the probabilities of noise parameters. Estimation of potential process change cost due to potential design parameter value changes is also studied. A case study in pipeline engineering design and construction has been conducted to demonstrate the effectiveness of this new parameter design approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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