Application of the fault tree analysis for assessment of the power system reliability
Bibliographic record
Abstract
Technical Programme Committee Ale B, The Netherlands Badia G, Spain Alemano A, Luxembourg Barros A, France Amari S, United States Bartlett L, United Kingdom Andersen H, Denmark Basnyat S, France Aneziris O, Greece Birkeland G, Norway Antao P, Portugal Bladh K, Sweden Arnaiz A, Spain Boehm G, Norway Bris R, Czech Republic Le Bot P, France Bründl M, Switzerland Limbourg P, Germany Burgherr P, Switzerland Lisnianski A, Israel Bye R, Norway Lucas D, United Kingdom Carlos S, Spain Luxhoj J, United States Castanier B, France Ma T, United Kingdom Castillo E, Spain Makin A, Australia Cojazzi G, Italy Massaiu S, Norway Contini S, Italy Mercier S, France Cozzani V, Italy Navarre D, France Cha J, Korea Navarro J, Spain Chozos N, United Kingdom Nelson W, United States De Wit S, The Netherlands Newby M, United Kingdom Droguett E, Brazil Nikulin M, France Drottz-Sjoberg B, Norway Nivolianitou Z, Greece Dutuit Y, France Pérez-Ocón R, Spain Escriche I, Spain Pesme H, France Faber M, Switzerland Piero B, Italy Fouladirad M, France Pierson J, France Garbatov Y, Portugal Podofillini L, Italy Ginestar D, Spain Proske D, Austria Grall A, France Re A, Italy Gucma L, Poland Revie M, United Kingdom Hardman G, United Kingdom Rocco C, Venezuela Harvey J, United Kingdom Rouhiainen V, Finland Hokstad P, Norway Roussignol M, France Holicky M, Czech Republic Sadovsky Z, Slovakia Holloway M, United States Salzano E, Italy Iooss B, France Sanchez A, Spain Iung B, France Sanchez-Arcilla A, Spain Jonkman B, The Netherlands Scarf P, United Kingdom Kafka P, Germany Siegrist M, Switzerland Kahle W, Germany Sørensen J, Denmark Kleyner A, United States Storer T, United Kingdom Kolowrocki K, Poland Sudret B, France Konak A, United States Teixeira A, Portugal Korczak E, Poland Tian Z, Canada Kortner H, Norway Tint P, Estonia Kosmowski K, Poland Trbojevic V, United Kingdom Kozine I, Denmark Valis D, Czech Republic Kulturel-Konak S, United States Vaurio J, Finland Kurowicka D, The Netherlands Yeh W, Taiwan Labeau P, Belgium Zaitseva E, Slovakia Zio E, Italy Webpage Administration Alexandre Janeiro Instituto Superior Técnico, Portugal Local Organizing Committee Sofía Carlos Alberola Universidad Politécnica de Valencia Eva Ma Doménech Antich Universidad Politécnica de Valencia Antonio José Fernandez Iberinco, Chairman Reliability Committee AEC Blás Galván González Universidad de Las Palmas de Gran Canaria Aitor Goti Elordi Universidad de Mondragón Sebastián Martorell Alsina Universidad Politécnica de Valencia Rubén Mullor Ibañez Universidad de Alicante Rafael Pérez Ocón Universidad de Granada Ana Isabel Sánchez Galdón Universidad Politécnica de Valencia Vicente Serradell García Universidad Politécnica de Valencia Gabriel Winter Althaus Universidad de Las Palmas de Gran Canaria Conference Secretariat and Technical Support at Universidad Politécnica de Valencia Gemma Cabrelles López Teresa Casquero García Luisa Cerezuela Bravo Fanny Collado López María Lucía Ferreres Alba Angeles Garzón Salas María De Rus Fuentes Manzanero Beatriz Gómez Martínez José Luis Pitarch Catalá Ester Srougi Ramón Isabel Martón Lluch Alfredo Moreno Manteca Maryory Villamizar Leon José Felipe Villanueva López Sponsored by Ajuntament de Valencia Asociación Española para la Calidad (Comité de Fiabilidad) CEANI Generalitat Valenciana Iberdrola Ministerio de Educación y Ciencia PMM Institute for Learning Tekniker Universidad de Las Palmas de Gran Canaria Universidad Politécnica de Valencia
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".