Predicting climate change effects on wildfires requires linking processes across scales
Bibliographic record
Abstract
Abstract Accurate process‐based prediction of climate change effects on wildfires requires coupling processes across orders of magnitude of time and space scales, because climate dynamic processes operate at relatively large scales (e.g., hemispherical and centennial), but fire behavior processes operate at relatively small scales (e.g., molecules and microseconds). In this review, we outline some of the current understanding of the processes by which climate/meteorology controls wildfire behavior by focusing on four critical stages of wildfire development: (1) fuel drying, (2) ignition, (3) spread, and (4) extinction. We identify some key mechanisms that are required for predicting climate change effects on fires, as well as gaps in our understanding of the processes linking climate and fires. It is currently not possible to make accurate predictions of climate change effects on wildfires due to the limited understanding of the linkage between general circulation model outputs and the local‐scale meteorology to which fire behavior processes respond. WIREs Clim Change 2011 2 99–112 DOI: 10.1002/wcc.92 This article is categorized under: Paleoclimates and Current Trends > Earth System Behavior
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".