The 2017 North Bay and Southern California Fires: A Case Study
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
Two extreme wind-driven wildfire events impacted northern and southern California in late 2017 leading to 46 fatalities and thousands of structures lost. This study describes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies the rarity of such conditions over the observational record. Both extreme wildfire events featured fire-weather metrics that were unprecedented in the observational record in addition to a sequence of climatic conditions that preconditioned fuels. The North Bay fires that affected portions of northern California in early October occurred coincident with strong downslope winds. The vast majority of the fires’ devastating effects and acres burned occurred overnight and within the first twelve hours of ignition. By contrast, the southern California fires of December were characterized by the longest Santa Ana wind event on record and included the largest wildfire in California’s history. Both fire events occurred following an exceptionally wet winter that was preceded by the drought of record in California. Fuels were further preconditioned as the warmest summer and autumn on record occurred in northern and southern California, respectively. Accelerated curing of fuels coupled with the delayed onset of autumn precipitation allowed for critically low dead fuel moisture leading up to the foehn wind events. Fire weather conditions were well forecasted several days prior to the fire. However, the rarity of fire-weather conditions that occurred in the wildland urban interface, along with other societal factors were key contributors to wildfire impacts to communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.023 |
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