3rd European Symposium on Fire Safety Science
Bibliographic record
Abstract
Pascal BOULET 1 , Bart MERCI 2 and Patrick VAN HEES 3 1 Université de Lorraine, CNRS, LEMTA, France 2 Ghent University, Department of Fluid Mechanics, Heat and Combustion, Ghent, Belgium 3 Lund University, Division of Fire Safety Engineering, Lund, Sweden pascal.boulet@univ-lorraine.fr EDITORIAL This special issue is based on the papers selected for presentation during the 3 rd ESFSS (European Symposium on Fire Safety Science) , held in Nancy (France) from 12 to 14 September 2018. Following the conference held in Cyprus (2015), the 3rd ESFSS was the third edition of a series of symposia organized in Europe, with the participation and support of the International Association for Fire Safety Science (IAFSS). The aim was to gather researchers from within Europe and beyond to have exchanges and discussions about fire safety science. In this frame, a workshop Agenda 2030 for a Fire Safe World was also organized by IAFSS on the afternoon of Tuesday, September 11, prior to the symposium, with the aim to discuss the definition of a Fire Safety Mission and the identification of specific topics for fire science research. Around 120 contributions have been received during the conference preparation, involving both submissions for oral and poster presentation. A selection, based on two peer-reviews of full papers, finally conducted to the acceptance of 35 contributions for oral presentation and 55 for poster presentation, plus 11 posters in a special session dedicated to work-in-progress. The conference was attended by 140 scientists, coming not only from Europe but from around the world, namely from Australia, Belgium, Canada, China, Czech Republic, Finland, France, Germany, India, Indonesia, Ireland, Japan, Portugal, Russia, Slovenia, Spain, Sweden, Turkey, United Kingdom, USA. List of Symposium chair and Scientific committee are available in this PDF.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.125 | 0.073 |
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".