Proceedings of the 7th International Conference on Integrity-Reliability-Failure (IRF2020)
Bibliographic record
Abstract
IRF2020 is the seventh international gathering of a prestigious series of Integrity-Reliability-Failure conferences coordinated by the International Scientific Committee of Mechanics and Materials in Design. This series of conferences is wholly devoted to advances in mechanics, materials, structural integrity and design. IRF2020 is jointly sponsored by the University of Porto, the University of Toronto and the Portuguese Society of Experimental Mechanics. The conference attracted over 250 contributions, with 216 accepted submissions involving 593 authors from 36 different countries around the world. The conference themes, which address novel and advanced topics on Integrity, Reliability and Failure, focused on Theory, Experiments and Applications in Engineering, including Computational Mechanics, Experimental Mechanics, Fracture and Fatigue, Composite and Advanced Materials, Tribology and Surface Engineering, Mechanical Design and Prototyping, Biomechanical Applications, Civil Engineering Applications, Energy and Thermo-Fluid Systems, and Industrial Engineering and Management, among other topics. Unfortunately, due to the serious restrictions imposed by the pandemic COVID-19, which is affecting people all around the world, the oral and poster presentations scheduled for the IRF2020 conference had to be cancelled from its traditional model, as it was planned taking place in Funchal, from 6 to 10 September 2020. Nevertheless, given the advanced stage of the whole editorial process, we decided to go ahead with the publication of the present volume of the conference Proceedings, which includes all the Extended Abstracts / Full Papers approved by the IRF2020 International Scientific Committee. The document is published online for free access, from where an e-book and a PDF version of the Proceedings can also be downloaded. We do believe that the publication of this volume offers our authors a forum for discussion and dissemination of their recent work in assessing the integrity, reliability and failure of engineering structures, components and systems. It fostered research that integrates mechanics and materials in the design process, and promoted exchange of ideas and international co-operation among scientists and engineers in this important field of engineering. We are particularly indebted to the authors and special guests for their contributions. Each of the 216 approved papers offers an opportunity for thorough discussions with the authors and the scientific community. Particularly, we acknowledge the excellent contributions of the participants, their innovative ideas and research directions, the novel modelling and simulation techniques, and the invaluable critical comments that will come out of this kind of “virtual” conference. We also take this opportunity to thank the members of the International Scientific Committee and the reviewers for their time and helpful suggestions, the symposia organizers for their efforts and valuable contributions to the success of this publication, and the local organizing committee for an absolutely superb organization of this “virtual” meeting. To all of them, we offer our deepest gratitude.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.143 | 0.090 |
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".