Bibliographic record
Abstract
Unprecedented times!We said this during the last two editions in 2020 and 2021, hoping that 2022 would allow us to meet again in person, and we are happy that we could finally make it!Welcome to the 28th IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS) 2022, held as a hybrid event at Politecnico di Torino, Italy, on September 12-14, 2022.This is an exciting year for the symposium, which maintains its enlarged scope to include all aspects of Robust System Design.IOLTS is in the second quarter of a century of its life.The established online testing technical meeting that became a symposium in 2003 has been held since then (2003 to 2019) in Kos Island (Greece), Funchal, Madeira Island (Portugal), Saint-Raphaël, French Riviera (France), Lake of Como (Italy), Hersonissos-Heraklion, Crete (Greece), Rhodes Island (Greece), Sesimbra (Portugal), Corfu (Greece), Athens (Greece), Sitges (Spain), Chania, Crete (Greece), Platja d'Aro (Spain), Elia, Halkidiki (Greece), Sant Feliu de Guixols, (Spain), Thessaloniki (Greece), Platja d'Aro (Spain), and Rhodes Island (Greece).All previous locations offered a combination of natural beauty and vividness, creating an ideal atmosphere for fertilizing debates and inspiring new ideas and solutions.These locations continued the traditions established during the first years when the technical meeting was run as a more informal workshop from 1995 to 2002.After two virtual editions in 2020 and 2021, when we missed the unique combination of beautiful landscape and active people interaction that is one of the strengths of this symposium, we are happy to land in the beautiful landscape of Torino, the first capital of Italy, renowned for the quality of food and wine, and home of Juventus.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".