The 2017 North Bay and Southern California Fires: A Case Study
Bibliographic record
Abstract
Two extreme wind-driven wildfire events impacted California in late 2017, leading to 46 fatalities and thousands of structures lost. This study characterizes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies their rarity over the observational record. Both events featured key fire-weather metrics that were unprecedented in the observational record that followed a sequence of climatic conditions that enhanced fine fuel abundance and fuel availability. The North Bay fires of October 2017 occurred coincident with strong downslope winds, with a majority of burned area occurring within the first 12 hours of ignition. By contrast, the southern California fires of December 2017 occurred during the longest Santa Ana wind event on record, resulting in the largest wildfire in California’s modern history. Both fire events occurred following an exceptionally wet winter that was preceded by a severe four-year drought. Fuels were further preconditioned by the warmest summer and autumn on record in northern and southern California, respectively. Finally, delayed onset of autumn precipitation allowed for critically low dead fuel moistures leading up to the wind events. Fire weather conditions were well forecast several days prior to the fire. However, the rarity of fire-weather conditions that occurred near populated regions, along with other societal factors such as limited evacuation protocols and limited wildfire preparedness in communities outside of the traditional wildland urban interface were key contributors to the widespread wildfire impacts.
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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 | 0.001 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".