Proceedings of the 4<sup>th</sup> European Symposium on Fire Safety Science (ESFSS 2024)
Bibliographic record
Abstract
EDITORIAL This special issue is based on the papers selected for presentation during the 4 th ESFSS (European Symposium on Fire Safety Science), held in Barcelona, Spain from October 9 to 11, 2024. Following the previous conference held in Nancy in 2018, the European Symposium on Fire Safety Science was the fourth edition in these series of symposia organized in Europe. The aim was to bring together researchers from Europe and beyond for exchanges and discussions on fire safety science. Approximately 200 contributions were received during the conference preparation, including submissions for both oral and poster presentations. Following a rigorous selection process involving at least 2 peer reviews of the full papers, 45 contributions were accepted for oral presentation and 99 for poster presentation, with 23 of the latter dedicated to work-in-progress. The conference was attended by 180 scientists from around the world, including countries such as Australia, Belgium, Canada, Chile, China, Czech Republic, Denmark, Finland, France, Germany, Hong Kong, India, Italy, Japan, Republic of Korea, Malaysia, New Zealand, Norway, Poland, Slovenia, South Africa, Spain, Sweden, Switzerland, the United Kingdom, Turkey, and the United States. This volume includes all the papers from the contributions presented during the seminar. They are organized into six main topics, in line with the conference program: • Material behaviour in fire (ignition, pyrolysis, flame spread, smouldering) • Fire dynamics, structures in fire (fire plumes, compartment fires, tunnel fires) • Wildland fires / Wildland-urban fires • Fire detection and suppression • Evacuation and human behaviour • Miscellaneous (e.g.: explosions and industrial fires, battery fires, solar panels, fire codes and standards) The conference organizers gratefully acknowledge the members of the scientific committee and the experts who reviewed the papers. They also extend their thanks to ACTIVA Congresos and the local organizing committee for their efforts in preparing the conference. The support from the IAFSS, the sponsors (SODECA; Kingspan. OFR Consultants, FM Global, Efectis and CSTB) the UPC research group CERTEC, and the Universitat Politècnica de Catalunya is also acknowledged. List of Syposium chairs, Organizing committee, Local organizing committee 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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".