Guest Editorial: Stochastic models in reliability engineering, life sciences and operations management
Bibliographic record
Abstract
This special issue is devoted to papers from the International Symposium on {\it Stochastic Models in Reliability Engineering, Life Sciences and Operations Management (SMRLO'10)}. The Symposium was held at the Sami Shamoon College of Engineering, Beer Sheva, Israel in February 2010. The idea of the Symposium was to assemble researchers and practitioners from universities, institutions and industries, working in these fields. Theoretical issues and applied case-studies presented in the Symposium, ranged from academic considerations to industrial applications. Presenters came from twenty nine countries from around the world: Australia, Belarus, Bulgaria, Canada, China, Cyprus, Czech Republic, France, Germany, Greece, India, Ireland, Israel, Italy, Latvia, The Netherlands, Poland, Romania, Russia, Singapore, Spain, Sweden, Switzerland, Taiwan, Turkey, Ukraine, United Kingdom, USA, Uzbekistan. This clearly shows the international nature of this Symposium. One hundred and eighty six papers were accepted for presentation at the conference and publication in the Symposium Proceedings. These articles were later reviewed for possible extension and inclusion in this special issue. Authors of eight (8) articles were invited to submit their work for publication in this issue of the transdisciplinary international journal {\it Mathematics in Engineering, Science and Aerospace}. We hope that this selection of papers gives an idea of the diversity of topics covered in the Symposium.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".