Drought triggers and sustains overnight fires in North America
Bibliographic record
Abstract
Abstract Overnight fires are emerging in North America with yet unknown drivers and implications. This phenomenon is significant as it challenges the traditional understanding of the “active day, quiet night” paradigm of the diurnal fire cycle and current fire management practices. Here we show that ~20% of all large fires (≥1000ha) in North America observed during 2017-2020 using geostationary satellite images and terrestrial fire records burned through a total of 1,084 nights. Overnight burning was characterized by early onset after ignition, high persistence, and a tendency to mutually reinforce extreme fires. While warming weakens the climatological barrier to general nighttime fires1, we found the major driver of overnight burning was the accumulated fuel dryness and fuel availability (i.e., drought conditions), rather than fast-reacting day-night weather fluctuations. Drought conditions tended to sustain overnight burning for periods of multiple days, and even weeks. Moreover, we show that daytime drought indicators can be used to predict overnight burning events, which could facilitate early detection and management of nighttime fires. Recently observed and predicted future increasing trends in conditions conducive to overnight burning indicate that disruption of the diurnal fire cycle may accelerate, leading to larger, more intense, and extreme fires in future.
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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 | 0.000 |
| 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) | 0.002 | 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".