Ecoregion based attribution analysis of the influence of several fire danger indices on the amount of burned area at a global scale by means of pseudo transfer entropy
Why this work is in the frame
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Bibliographic record
Abstract
Understanding the current fire-climate relationships is of utmost importance in order to assess the potential impacts that projected climate may exert in the near future. However, the many factors involved in fire activity often prevent a proper attribution of the observed variability. Unlike the many previous correlative studies, here we address this problem using a 'causality' measure known as “pseudo transfer entropy†(pTE), relating three widely used fire danger indices to the global observed burned areas (namely the Canadian Fire Weather Index, the Fire Danger Index from the Australian McArthur Mark 5 Rating System and the Burning Index from the U.S. Forest Service National Fire-Danger Rating System). The study has been performed at an spatial aggregation level defined by the RESOLVE ecoregions, attending to their homogeneous fuel and climatic properties, and considering different spatial and temporal aggregation statistics (mean, 90th percentile, 95th percentile and sum). We present an open, web-based interactive tool to explore and compare the results derived from the causality of these different fire danger indices with the observed burned area. Our results unveil some consistent patterns and three main conclusions can be drawn: 1) in the overall, all indices exhibit a similar performance in explaining observed burned areas, although regional differences may justify the selection of one index over another in regional studies, 2) the aggregation method used at the ecoregion level affects the causality results, with higher percentiles being better explained by pTE than the mean state and 3) the interactive tool designed may serve as a valuable method of intercomparison and analysis for the vulnerability and impact assessment community involved in fire research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it