Large Outdoor Fires and the Built Environment (LOF&BE): Summary of Virtual Workshop
Bibliographic record
Abstract
Two virtual workshops of the permanent working group, sponsored by the International Association for Fire Safety Science (IAFSS), entitled Large Outdoor Fires and the Built Environment (LOF&BE), were held this past August (2020). The first session was held on August 4, 2020 with times selected to suit those in Africa, Europe, and Asia/Oceania. The second session was held on August 6, 2020 with times to suit those in North and South America. The Ignition Resistant Communities (IRC) subgroup is focused on developing the scientific basis for new standard testing methodologies indicative of large outdoor fire exposures, including the development of necessary testing methodologies to characterize wildland fuel treatments adjacent to communities. IRC subgroup progress was presented by Alex Filkov (U Melbourne) and Daniel Gorham (IBHS). The Emergency Management and Evacuation (EME) subgroup is focused on developing the scientific basis for effective emergency management strategies for communities exposed to large outdoor fires. EME subgroup progress was presented by Maria Theodori (Reax Engineering Inc.) and Sayaka Suzuki (NRIFD). The Large Outdoor Fire Fighting (LOFF) subgroup is providing a review of various tactics that are used, as well as the various personal protective equipment (PPE), and suggests pathways for research community engagement, including environmental issues in suppressing these fires. LOFF subgroup progress was presented by Xinyan Huang (Hong Kong Poly U) and Brian Lattimer (Va Tech).
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) | 0.001 | 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".