THEORETICAL MODEL ADEQUACY CREATION AND EVALUATION CONCERNINGTHE RELIABILITY OF QUARTER PIPELINES IN THE CITY OF KAZAN
Bibliographic record
Abstract
It is necessary to be able to predict the number of pipeline failures over time for a reasonable choice of relocation volumes and pipeline repair. At present, several methods are known to design reliability models for pipeline transport systems. The urgency of the problem concerning the provision of quarter heat network reliability is determined by the fact that the greatest number of failures is observed on them according to statistics. The object of the study is the quarter heat networks of the city of Kazan, the subject of investigation is the regularities of pipeline failure occurrence. The method of a theoretical reliability model design is used in the work on the basis of development probability statistical distribution to failure. The primary information is the data on the failures of quarter pipelines in hot water supply networks of the city of Kazan.Purpose and objectives of the study. The aim of the work is to build and assess the adequacy of the reliability model concerning the quarter pipelines in Kazan according to the statistical data of heat network failures.The model of quarter pipelines reliability of the city of Kazan is developed and justified. The calculation of the empirical probability of failure-free operation of pipelines and failure rates in 2007-2011 was carried out. The use of obtained theoretical model will allow to predict the number of heat network failures in the future. The model can be used to justify the volume of quarter heating network relocation in various power districts of the city.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".