Proceedings from the Eighth International Symposium on Tunnel Safety and Security, Borås, Sweden, March 14-16, 2018
Bibliographic record
Abstract
This report includes the Proceedings of the 8th International Symposium on Tunnel Safety and Security (ISTSS) held in Borås, Sweden, 14-16th of March, 2018. The Proceedings include 41 papers given by session speakers and 16 extended abstracts presenting posters exhibited at the Symposium. The papers were presented in 12 different sessions. Among them are Fire Safety Engineering: Cases & Incidents, Fire Safety Engineering: The Aims, Fire Detection, Explosions, Risk Analysis, Fire Safety Engineering: Case studies, Ventilation, Fire Safety Engineering: State of the Art, Fire Dynamics, Fixed Fire Fighting Systems (FFFS) and Evacuation and Human Behavior. Each day was opened by invited Keynote Speakers (in total six) addressing broad topics of pressing interest. The Keynote Speakers, selected as leaders in their field, consisted of Hans Brun, the Swedish Defence University, Dr Iain Bowman, Mott MacDonald, Canada, Dr Ying Zhen Li, RISE Research Institutes of Sweden, Dr Johan Lundin, WSP, Sweden, Allan Skovlund, Greater Copenhagen Fire Department, Denmark and Prof David Purser, Hartford Environmental Research, UK. We are grateful that the keynote speakers were able to share their knowledge and expertise with the participants of the symposium.
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 | 0.002 | 0.002 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.242 | 0.077 |
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".