GlobalRx: A global assemblage of regional prescribed burn records
Bibliographic record
Abstract
File Name File Type Description ERA5_CEMS_Download_and_Resample_Notebooks.zip ZIP file containing Python Jupyter notebooks Code used to download and resample ERA5 and CEMS meteorological data from hourly into daily values Geolocate_GlobalRx_Notebooks.zip ZIP file containing Python Jupyter notebooks Code used to determine values of meteorological and environmental variables at date and location of each burn record GlobalRx-Figures-Stats.ipynb Jupyter notebook Code used to calculate and generate all statistics and figures in the paper GlobalRx_CSV_v2024.1.csv GlobalRx_XLSX_v2024.1.xlsx GlobalRx_SHP_v2024.1.zip CSV, Excel, and ZIP file containing shape file and accompanying feature files GlobalRx dataset. Features of the dataset are described in more detail below.** summary_table_country_biome_GlobalRx.xlsx summary_table_country_fuelbed_GlobalRx.xlsx summary_table_country_burned_area_hist_GlobalRx.xlsx Excel files Summary tables containing counts of the number of records for all biomes, fuelbed classifications, and burned area size ranges for each country **Description of GlobalRx Dataset: 204,517 records of prescribed burns in 16 countries. In the information below, the name of the variable's column within the dataset is given in parentheses () in code font. For example, the column with the Drought Code data is titled DC. For each record, the following general information (derived from the original burn records sources) is included, where available: Latitude (Latitude) Longitude (Longitude) Year (Year) Month (Month) Day (Day) Time* (Time) DOY (DOY) Date (Date) Country (Country) State/Province (State/Province) Agency/Organisation (Agency/Organisation) Burn Objective* (Burn Objective) Area Burned (Ha)* (Area Burned (Ha)) Data Repository (Data Repository) Citation (Citation) * Not available for every record For each record, the following meteorological information (derived from the ERA5 single levels reanalysis product) is also included: Daily total accumulated precipitation (PPT_tot) Daily minimum and mean relative humidity (RH_min, RH_mean)* Daily maximum 2-meter temperature (T_max,T_mean) Daily maximum and mean 10-meter wind speed (Wind_max, Wind_mean) Daily minimum boundary layer height (BLH_min) C-Haines Index (CHI)* Vapor pressure deficit (VPD)* * Computed from other ERA5 meteorological variables. For each record, the following fire weather indices and components (derived from ERA5 fire weather reanalysis product) are also included: Canadian fire weather index (FWI) Fine fuel moisture code (FFMC) Drought moisture code (DMC) Drought code (DC) McArthur forest fire danger index (FFDI) Keetch-Byram drought index (KBDI) US burning index (USBI) For each record, the following environmental information (derived from various sources, see paper for more information) is also included: Ecoregion (Olson et al. 2001) (Ecoregion (Olson)) Biome (Olson et al. 2001) (Biome (Olson)) Koppen Climate (Beck et al. 2023) (Koppen Climate) Topography (Danielson and Gesch 2011) (Topography) Fuelbed Classification (GFD-FCCS) (Pettinari and Chuvieco 2016) (Fuelbed Classification (GFD-FCCS)) Fuelbed Group, broader groupings of the Fuelbed Classification used in the summary tables above (Fuelbed Group) WDPA Name (WDPA 2024) (WDPA Name) WDPA Governance (WDPA 2024) (WDPA Governance) WDPA Ownership (WDPA 2024) (WDPA Ownership) WDPA Designation (WDPA 2024) (WDPA Designation) WDPA IUCN Category (WDPA 2024) (WDPA IUCN Category)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.038 |
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".