An analysis of spotting distances during the 2017 fire season in the Northern Rockies, USA
Bibliographic record
Abstract
The wildfires that burned in the Northern Rockies region of the USA during the 2017 fire season provided an opportunity to evaluate the suitability of using broadscale and temporally limited infrared data on hot spot locations to determine the influence of several environmental variables on spotting distance. Specifically, correlations between the maximum observed spot fire distance for each unique combination of fire and day and geo-referenced environmental data on wind speed, vegetation, and terrain, along with specific fire characteristics (size, fire perimeter shape, and growth), were assessed. The data were also utilized to evaluate a popular theoretical model developed by Albini (1979) for predicting the maximum spotting distance for single and group tree torching. The results suggested a significant positive correlation between the maximum observed spot fire distance and an interaction between fire growth and wind speed. Significant negative correlations between maximum spotting distance and fire perimeter shape, canopy height, and terrain steepness were also discovered. The evaluation of Albini’s (1979) model suggested that selecting a high estimate of potential wind speed was important to minimize the likelihood of underpredicting maximum spotting distance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".