Statistical Analysis and Prediction of the Product Complaints
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 The article presents the results of the analysis of cardboard packaging complaints based on selected quality tools and statistical tools for the purpose of a rough assessment of the effectiveness of corrective and preventive actions taken by the surveyed company and for predictive purposes. The analysis was performed in terms of two research periods - 1 year and quarters, and from the point of view of total complaints and external - customer complaints. Data on the number of products complained of as well as financial losses incurred by the company on this account were analysed. The article presents the potential of both classic quality tools and statistical tools for the purposes of in-depth analysis of complaints data and for predictive purposes and subsequent risk analysis. The critical complaint was indicated - complaint code 403 - overprint. The number of complained products to be expected in the next quarter of the new year was determined. The article shows that the corrective and preventive actions taken by the company have not yet brought the expected result in the form of reducing the number of products complained by customers during the quarters surveyed.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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