NEX-GDDP-FWI: Downscaled 21st century global fire weather projections
Bibliographic record
Abstract
NEX-GDDP-FWI v1.0 The NEX-GDDP-FWI data was created from the python codes shared in this repository. The NASA Earth eXchange-Global Daily Downscaled Projections-Fire Weather Index (NEX-GDDP-FWI) is a dataset that provides global fire weather projections derived from daily Global Climate Model (GCM) simulations. The dataset uses the Canadian Forest Fire Weather Index System (CFWIS) framework to estimate fire danger by considering the effects of fuel moisture and wind on fire behavior and spread. The dataset includes retrospective (1950-2014) and prospective (2015-2100) simulations from 33 GCMs participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socio-economic Pathways (SSPs). To make the dataset more accessible, the fire weather metrics are summarized at coarser temporal scales (monthly and annually), and source codes are provided for investigating daily fire weather. Additionally, Multi-Model Ensemble (MME) datasets are provided for each fire weather metric, which include monthly and annually summarized fire weather metrics. The total data volume of the dataset is approximately 4.5 TB and is available through the NASA Advanced Supercomputing (NAS) Data Portal. Here are brief explanations for the python codes: FWIfunctions.py: This python code has all sub-component functions used in the CFWIS framework. calFireIndex.py: This python code is a main code reading input NEX-GDDP data and compute sub-components of the CFWIS framework. get_MME_FWI_Monthly_release.py: This python code is for summarizing daily fire weather indices of individual GCM to multi-model ensemble at monthly time step get_MME_FWI_Yearly_release.py: This python code is for summarizing daily fire weather indices of individual GCM to multi-model ensemble at yearly time step
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".