Atmospheric and Fuel Moisture Characteristics Associated with Lightning-Attributed Fires
Bibliographic record
Abstract
Abstract A systematic examination is presented of the relationship between lightning occurrence and fires attributed to lightning ignitions. Lightning occurrence data are matched to a database of fires attributed to lightning ignition over southeastern Australia and are compared with atmospheric and fuel characteristics at the time of the lightning occurrence. Factors influencing the chance of fire per lightning stroke are examined, including the influence of fuel moisture and weather parameters, as well as seasonal and diurnal variations. The fuel moisture parameters of the Canadian Fire Weather Index System are found to be useful in indicating whether a fire will occur, given the occurrence of lightning. The occurrence of “dry lightning” (i.e., lightning that occurs without significant rainfall) is found to have a large influence on the chance of fire per lightning stroke. Through comparison of the results presented here with the results of studies from other parts of the world, a considerable degree of universality is shown to exist in the characteristics of lightning fires and the atmospheric conditions associated with them, suggesting the potential for these results to be applied more widely than just in the area of the study.
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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 | 0.002 | 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) | 0.001 | 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".