Code for Projected Canadian Forest Fire Danger Rating System (CFFDRS) metrics within Fire Danger Rating Areas in Alaska (1980–2099) (version 1.0.0)
Bibliographic record
Abstract
Effective future fire planning and decision-making in Alaska requires accessible, interpretable, and locally relevant climate data to anticipate fire danger. To meet this need, we utilized an existing dataset of projected fire danger metrics from the Canadian Forest Fire Danger Rating System (Young et al. 2025). This dataset contains daily projections from 1980 through 2099 for key metrics including Buildup Index, Drought Code, Duff Moisture Code, Fine Fuel Moisture Code, Fire Weather Index, and Initial Spread Index. These projections were generated using historical data and outputs from four climate models, all downscaled to a 0.25-degree resolution. We developed Python and R code to process and visualize the data, producing summary tables, time series plots, and maps. Outputs primarily focused on 16 Fire Danger Rating Areas in Alaska, which correspond to the boreal forest extent covered by the input dataset. The code provides a transparent and reproducible methodology, enabling the generation of customized products that deliver actionable fire danger insights to support fire planning and management under future climate scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.044 |
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".