Temperate and boreal forest mega‐fires: characteristics and challenges
Bibliographic record
Abstract
Mega‐fires are often defined according to their size and intensity but are more accurately described by their socioeconomic impacts. Three factors – climate change, fire exclusion, and antecedent disturbance, collectively referred to as the “mega‐fire triangle” – likely contribute to today's mega‐fires. Some characteristics of mega‐fires may emulate historical fire regimes and can therefore sustain healthy fire‐prone ecosystems, but other attributes decrease ecosystem resiliency. A good example of a program that seeks to mitigate mega‐fires is located in Western Australia, where prescribed burning reduces wildfire intensity while conserving ecosystems. Crown‐fire‐adapted ecosystems are likely at higher risk of frequent mega‐fires as a result of climate change, as compared with other ecosystems once subject to frequent less severe fires. Fire and forest managers should recognize that mega‐fires will be a part of future wildland fire regimes and should develop strategies to reduce their undesired impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".