IX International Conference on Forest Fire Research and 17th International Wildland Fire Safety Summit: special issue introduction (Part 4)
Bibliographic record
Abstract
The 9th International Conference on Forest Fire Research, organised by the Forest Fire Research Center of the Association for Developmental of Industrial Aerodynamics every 4 years since 1990, was held in November 2022 in Coimbra, Portugal. The conference was held in conjunction with the 17th International Wildland Fire Safety Summit, sponsored by the International Association of Wildland Fire. The number and quality of the submissions for this joint event was very high, and the authors were encouraged to submit a full paper to a special issue of the International Journal of Wildland Fire (IJWF). Given the large number of submissions, the Journal decided to publish the special issue in four parts. Part 1 was published in January 2023, with eight papers, Part 2 in March 2023, with 10 papers and Part 3 in June 2023 with 15 papers. This fourth part presents 10 original papers, in three areas: Decision Support Systems and Tools (3), Risk Assessment & Reduction (3) and the Wildland Urban Interface (4). All the papers in this special issue are published Open Access.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.168 | 0.097 |
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".