GlobalRx: A global assemblage of regional prescribed fire records for use in assessments of climate change impacts
Bibliographic record
Abstract
Prescribed (Rx) and controlled fires are an important land management tool used globally for a variety of reasons, including the reduction of hazardous fuel loads, ecological conservation, agriculture, and natural resource management. Its use has important implications for wildfire risk, biodiversity, and carbon storage. However, the use of Rx and controlled fires is highly dependent upon weather conditions, requiring a weather window during which a careful balance of temperature, moisture, and wind ensure that the burns achieve their objectives while minimizing ecological damage or risk to human lives or assets. The planning and execution of Rx burns must also consider how these weather conditions interact with the local vegetation and ecology. As fire weather is projected to grow more extreme under the impacts of climate change, there is a growing need to monitor this effect on the ability to carry out Rx burning.Here, we introduce a new dataset, GlobalRx, which includes around 140,000 records of Rx and other controlled fires from 16 countries, encompassing 207 ecoregions and 13 biomes around the world. For each record, we have geolocated values of various metrics of fire weather and fire danger (e.g. fire weather indices, vapour pressure deficit) from the ERA5 meteorological reanalysis, as well as the biome, ecoregion, fuelbed type, and protected area status from global thematic layers. We demonstrate the usefulness of this dataset for analyzing viable meteorological windows under which Rx fires may be conducted across diverse environmental settings in the present climate, as well as how these Rx burning windows may shift under the threats of climate change. This dataset has potential to shed light on how Rx burning windows may shift under future climate change, as well as opportunities to understand other drivers and effects of Rx burning.This project has been supported by valuable contributions from non-public data from a consortium of data providers: Parks Canada, South Africa National Parks, Brazilian Institute of the Environment and Renewable Natural Resources, East-Pyrenees Prescribed Burning Team, Institute for Nature Conservation and Forests (Portugal), Regional Forest Fire Service (Italy), Russian Federal Forestry Agency, H2020 LifeTaiga Project, Government of the Principality of Asturias, Council of Andalucía, Council of Galicia, Forestry England, National Forestry Commission of Mexico, ZEBRIS Geo-IT GmbH, Hokkaido University, Pau Costa Foundation, Asian Forest Cooperation Organization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".