Bibliographic record
Abstract
The NEX-GDDP-FWI is a dataset that provides global fire weather projections derived from downscaled (0.25°) and bias-corrected daily Earth System Model (ESM) simulations. The dataset uses the Canadian Forest Fire Weather Index System framework to estimate fire danger by considering the effects of fuel moisture and wind on fire behavior and spread. It includes retrospective (1950-2014) and prospective (2015-2100) simulations from 33 ESMs. 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. Multi-Model Ensemble data of monthly and annual fire weather metrics are also provided. This publicly available 5.1 TB dataset has the potential to be broadly used in not only for wildfire risk assessment but also for various future climate change impact assessments and preparedness.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Global fire-weather projection dataset; a domain climate data product, not research infrastructure studied as an object.
This presents a fire-weather dataset for climate and wildfire applications, not research as its object.
Climate/fire-weather projection dataset product; not a study of research, scholarly infrastructure, or science as a system.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".