The New Product Online Evaluation by Expert Based on the Analytic Hierarchy Process 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
<p>This study analyzed an evaluation method on new product design over the internet. The expert evaluation is an essential part in product producing and consuming chain. The research takes the Tai Huoniao website (http://www.taihuoniao.com) as a thinking point, which used scoring on product design works. The research uses the analysis hierarchy process method in product evaluation, adding a quantitative analysis on expert-rating. The method also brings an influence on product design works in evaluating online. It is evident that the weight of criterion over the expert evaluation is strongly influenced consumer’s decision-making. By the way of imposing weight on criterion after expert evaluation, it provides an approach for designer to learn more about how can a new product be assessed on computer screen. The weighting on criterion supports the new product design efficiently. The aim of the research is to evaluate the product design criterion in AHP method so as to meet the consumers’ need.<strong></strong></p>
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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