Developing novel machine-learning-based fire weather indices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Accurate wildfire risk estimation is an essential yet challenging task. As the frequency of extreme fire weather and wildfires is on the rise, forest managers and firefighters require accurate wildfire risk estimations to successfully implement forest management and firefighting strategies. Wildfire risk depends on non-linear interactions between multiple factors; therefore, the performance of linear models in its estimation is limited. To date, several traditional fire weather indices (FWIs) have been commonly used by weather services, such as the Canadian FWI.@Traditional FWIs are primarily based on empirical and statistical analyses. In this paper, we propose a novel FWI that was developed using machine learning—the machine learning based fire weather index (MLFWI). We present the performance of the MLFWI and compare it with various traditional FWIs. We find that the MLFWI significantly outperforms traditional indices in predicting wildfire occurrence, achieving an area under the curve score of 0.99 compared to 0.62–0.80. We recommend applying the MLFWI in wildfire warning systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it