Foreword: Special issue on statistical reliability and maintenance modeling
Bibliographic record
Abstract
On December 2010, the Fourth Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling was held in Wellington, New Zealand. It attracted participants from countries such as Australia, Canada, China, Finland, France, Germany, India, Indonesia, Japan, Republic of Korea, Malaysia, New Zealand, Singapore, Sweden, Taiwan, UK, USA, and others. In this three-day symposium, researchers, scientists, and practitioners from different parts of the world were brought together to discuss the state of research and practice in reliability and maintainability. They identified important and challenging problems and worked together to discover possible solutions. Many new collaborations and research projects were initiated. In this special issue/section, we focus on statistical reliability and maintenance modeling in business and industry. We believe that each paper makes a useful contribution from a theoretical and/or practical viewpoint. This special issue/section consists of extended versions of papers presented at the Fourth Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling. It includes studies on age replacement/minimal repair policies for systems with random working times, semi-unified approach in constructing lifetime (aging) distributions, improved expectation–maximization algorithm for phase-type distributions with grouped and truncated data, identifying the optimal maintenance policy for non-renewing replacement–repair post-warranty period from users' point of view, and warranty/upgrade issues for second-hand products. Also, this special issue/section consists of a review/tutorial paper on methods and approaches for availability assessment of commercial systems. This paper reviews non-state space, state-space, and hierarchical availability models. It provides a useful guide for availability assessment through a number of case studies and shows the usefulness of stochastic modeling for quantifying system availability. The review/tutorial paper is followed up by two discussion papers and a rejoinder. We express our gratitude to Prof. Fabrizio Ruggeri, the Editor-in-Chief for the Applied Stochastic Models in Business and Industry journal, whose guidance and cooperation encouraged our work on the special issue/section and led to its publication.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".