Towards customer evaluation based product performance modeling
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
In the front-end customer driven design process, a number of design alternatives need to be evaluated based on customer expectations. A systematic method is presented in this paper to rank design alternatives by formulating a future rating model. Projections to Latent Structures (PLS), which is a multivariate analysis technique with proven efficiency, is employed to model experts' ratings in terms of product attributes for each time step, and screen unimportant attributes using Variable Importance (VIP) Scores. The PLS model parameter time series is used to formulate the future rating model, by means of an optimization algorithm embedded with forecasting. A case study is conducted using ratings of 16 major mobile phone technological forums collected from 2006 first quarter to 2009 third quarter. The future model predictions of 2009 fourth quarter, with Mean Absolute Percentage Error (MAPE) of 1.6, show the potential of proposed concept screening method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it