A global assemblage of regional prescribed burn records — GlobalRx
Bibliographic record
Abstract
Prescribed burning (RxB) is a land management tool used widely for reducing wildfire hazard, restoring biodiversity, and managing natural resources. However, RxB can only be carried out safely and effectively under certain seasonal or weather conditions. Under climate change, shifts in the frequency and timing of these weather conditions are expected but analyses of climate change impacts have been restricted to select few regions partly due to a paucity of RxB records at global scale. Here, we introduce GlobalRx, a dataset including 204,517 RxB records from 1979-2023, covering 16 countries and 209 terrestrial ecoregions. For each record, we add a comprehensive suite of meteorological variables that are regularly used in RxB prescriptions by fire management agencies, such as temperature, humidity, and wind speed. We also characterise the environmental setting of each RxB, such as land cover and protected area status. GlobalRx enables the bioclimatic range of conditions suitable for RxB to be defined regionally, thus unlocking new potential to study shifting opportunities for RxB planning and implementation under future climate.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".