[Evaluating fire behavior simulators in southwestern China forest area].
Bibliographic record
Abstract
As an important technical reference for efficient prevention and fighting against forest fire, forest fire behavior parameters are mainly obtained from fire behavior simulators in some deve-loped countries. This study selected two simulators, the Farsite from USA and the Prometheus from Canada, which were both widely used in local area. Through comparing simulated results and relative data of the '3·29 Fire' occurred in Anning City, Southwestern China, we tried to evaluate accuracy of the simulators in a quantitative way. The results indicated that, in the simulation of peri-meter, the precision of Farsite under Scott fuel model was the highest, while Prometheus was the lowest, but the difference was not significant. The difference in simulative perimeter between Farsite and Prometheus mainly concentrated in the distribution area of Pinus yunnanensis. In the simulation of rate of spread (ROS), the mean ROS results of Farsite under both fuel models were close to the actual situation, while the results of Prometheus were far away from the actual situation. The diffe-rent simulative area of ROS between Farsite and Prometheus mainly concentrated in the distribution area of P. yunnanensis. In the simulation of fireline intensity (FLI), the mean FLI results of Farsite under both fuel models were similar, and Prometheus obtained significantly different FLI results from Farsite, while the different simulative area of FLI between Farsite and Prometheus mainly concentrated in the distribution area of Quercus.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 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".