Characterizing temporal changes in forest fire ignitions: looking for climate change signals in a region of the Canadian boreal forest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The potential impact of climate change on forest fire risk is of significant concern. Postulated climate change effects on wildfires include increasing annual trends in ignitions and a lengthening of the fire season. We propose to use logistic generalized additive mixed models to investigate these characteristics. We present the modelling framework and outline a set of candidate models that are nested in terms of their fixed effects components. Model selection via likelihood ratio testing is discussed and connected to an entropy‐based scoring rule for Bernoulli responses. We illustrate its application using data for lightning‐caused forest fire ignitions over a period of 42 years in a 9 884 943 hectare region of boreal forest of northwestern Ontario, Canada. Seasonal and annual changes in ignition risk are observed and discussed, but we identify significant outstanding confounding factors that need to be addressed before one can assess the extent to which those changes can or cannot be attributed to climate change. Copyright © 2010 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it