Bibliographic record
Abstract
Over the last decade, communities across North America, including those in the Western United States, Canada, the Hawaiian Islands, and Texas, have repeatedly struggled to respond to increasingly destructive wildfire events. California experienced fires that consumed 1.5 million acres in 2017 and 1.9 million acres in 2018 across federal, state, and local jurisdictions, bringing the rolling five-year average of the annual burned area to more than one million acres (CAL FIRE, Statistics. https://www.fire.ca.gov/stats-events/ . Accessed 28 July 2022, 2024). Between 2017 and 2021, more than two million acres burned each year in California. Both the 2021 Dixie Fire (CAL FIRE, Dixie Fire Incident. https://www.fire.ca.gov/incidents/2021/7/13/dixie-fire . Accessed 12 May 2024, 2022) in the Sierra Nevada and Cascade Mountains in California and the 2024 Smokehouse Creek Fire (NASA, March 2, 2024 – Smokehouse Creek Fire scalds Texas. https://modis.gsfc.nasa.gov/gallery/individual.php?db_date=2024-03-02 . Accessed 12 May 2024, 2024) in the Texas Panhandle and western Oklahoma burned approximately one million acres each. As burned areas increase, so do the immediate and long-term consequences of the incidents, from the challenges faced by first responders and residents in the face of an oncoming fire to the economic and humanitarian toll inflicted by flood and infrastructure damage in the months and years afterward. There is an urgent need to invest in understanding the increasing scale of these problems, identify community-specific risk management and emergency response needs, and develop or apply innovative technologies, policies, and solutions to improve public safety and facilitate community reconstruction and resilience to wildfire events. This chapter presents a discussion of recent wildfire events affecting the Western United States and identifies a cross-section of issues affecting communities within and downslope of areas affected by wildfire events.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".