Bibliographic record
Abstract
This paper is dedicated to all the researchers for their contributions in reliability theories in the past 50 years. The paper provides a summary on the pioneers of reliability theories, and how their works placed a great influence on our reliability analysis today. This is also a survey paper on reliability theories and methods. The information provided in this paper is mostly based on literatures found first hand to provide as much a neutral view as possible. However, some of the information is adopted from Refs. 1-4. Area of interest in the reliability analysis included representation of reliability parameters, renewal theory, coherent structure, diagram-based models, theoretical methods, and other miscellaneous techniques. Diagram based models included block diagrams, fault tree analysis (FTA), event tree analysis, and flowgraphs. Theoretical methods included queueing theory, asymptotic analysis, Boolean algebra, Bayesian method, Monte Carlo simulation, optimization techniques. Miscellaneous methods that cannot be classified in any of the categories are also provided. Looking back in the last century, a lot of the contributions to reliability research were done in the last 50 years. Weibull, Epstein and Sobel had made a significant influence on the distribution functions we used today. Lotka, Campbell, Feller, Cox, Smith, Barlow, Proschan, Hunter, Marshall, Esary, Gnedenko, Belyaev, and Solov'yev had advanced the theories for reliability. Takacs' paper in sojourn time provided an initiative to the asymptotic studies. Birnbaum started a whole family on component importance measure for coherent structure.
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.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".