Application of the Taguchi Method to Sensitivity Analysis of a Middle-ear Finite-Element Model
Bibliographic record
Abstract
Sensitivity analysis of a model is the investigation of how outputs vary with changes of input parameters, in order to identify the relative importance of parameters and to help in optimization of the model. The one-factor-at-a-time (OFAT) method has been widely used for sensitivity analysis of middle-ear models. The results of OFAT, however, are unreliable if there are significant interactions among parameters. This paper incorporates the Taguchi method into the sensitivity analysis of a middle-ear finite-element model. Two outputs, tympanic-membrane volume displacement and stapes footplate displacement, are measured. Nine input parameters and four possible interactions are investigated for two model outputs. For the tympanic-membrane volume displacement, the contributions from the Young’s modulus and thickness of the pars tensa are dominant. For the footplate displacement, several input parameters play important roles and a strong interaction exists between the Young’s moduli of the incudomallear and incudostapedial joints. It is important to take this interaction into account when the parameters ' effects on model behaviour are being considered.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".