DETERMINATION OF THE QUALITY INDEX OF CARS
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 article presents theoretical studies of methods for assessing car quality indicators in the operation stage. Important criteria for determining the quality indicators of cars during the operation phase are the following: functional stability, ecology, comfort, technical solutions, and traffic safety. The problem of converting a multicriteria quality assessment to a single criterion is proposed to be solved by the method of determining a quality index. The methodology for the practical and actual implementation of this research is based on the evaluation of the quality index established on the average vehicle speed then the basic methodological principles are formulated. The quality index of a car is significantly dependent on the operating conditions. This article presents the correction coefficients for the quality index of base, hybrid, and electric vehicles, depending on the operating conditions. The studies and the proposed car quality index provide timely information on the characteristics of operating conditions, creating the necessary conditions and opportunities for automakers to improve the design of cars, promote the image of car brands, and increase sales.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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