Future changes in Simultaneous Megafires over the United States as projected by NA-CORDEX Simulations
Bibliographic record
Abstract
Simultaneous very large wildland fires present a unique challenge to fire management and firefighting resource allocation. Here we present potential changes in very large wildland fire simultaneity projected by an ensemble of regional climate model simulations produced for the North American Coordinated Regional climate Downscaling Experiment (NA-CORDEX) over multiple United States (U.S.) Geographic Area Coordination Centers (GACCs), the main regions over which wildland firefighting resources are coordinated. The NA-CORDEX simulations evaluated used the RCP8.5 future scenario, cover the years 1950-2100, and roughly span the range of climate sensitivity seen in the CMIP5 simulations. To calculate simultaneity, we fit generalized linear models (GLMs) with a negative binomial response to observational data to predict megafire simultaneity based on multiple fire weather indices per GACC. These indices include: KBDI (Keetch-Byram Drought Index), CFWI (Canadian Fire Weather Index), mFFWI, (modified Fosberg Fire Weather Index), ERC (Energy Release Component), BI (Burning Index), FM100, and FM1000 (100- and 1000-hour Fuel Moisture). The resulting GLMs for the best index-based predictors were then applied to the NA-CORDEX simulations. Future projections of changes in the probability of different levels of simultaneity centered on multiple future timeslices will be presented, along with the uncertainty associated with the choice of simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".