A model to predict lightning-caused fire occurrences
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
This paper presents a model to predict the probability that a lightning flash will lead to a detectable fire. This is done by estimating the probability of the lightning flash having a long-continuing current, the probability of ignition, the probability of survival, and the probability of arrival. Individual probabilities are calculated using the lightning, noon weather, and forest inventory data and combined to predict the number of ignitions, holdovers, and detectable fires within a region. The model was run for six fire seasons in Saskatchewan and predicted results were compared with the actual number of fires for that season. The model successfully predicted the number of fires on 55.7% of the days with a 64.8% detection rate and a false alarm rate of 29.7%. The model was found to be highly sensitive to moisture conditions, resulting in some unusually high predictions under dry conditions.
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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 | 0.000 | 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) | 0.000 | 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 it