Evaluation of Conceptual Designs and Maintainability Incorporating Uncertainty
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
This paper presents a method for carrying out a quantitative evaluation of design concepts with incomplete assessment information. When evaluating design solutions, in addition to consideration of product functionality, quality and cost, product life cycle performance such as maintainability should also be evaluated. Product maintainability issues are one of the more difficult design aspects to evaluate in early design stage. This paper describes specific maintainability metrics for evaluating product maintenance of conceptual design alternatives. Because of the uncertainty associated in early stage design evaluation, the varying degree of customer expectation must be incorporated into the evaluation system. Non-traditional fuzzy sets are used to represent expectations of the customer and compare them to design solution parameters. A case study is presented to illustrate the design method.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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