Dissemination of seasonal fire weather information for stakeholders and researchers
Bibliographic record
Abstract
Fire is the primary driving factor of the ecosystem dynamics of many forests, directly affecting the global carbon balance and atmospheric concentrations of the trace gases including carbon dioxide. Recent anthropogenic influence has led to an increase in frequency and impact of wild fires. Hence, it is of vital importance to predict forest fire risk at monthly and seasonal time scales in order to mitigate its impacts, including fire driven dynamics of ecosystem and socio-economic services. Resilience of the ocean–atmosphere system provides potential for early detection of upcoming fire season intensity. Here, we report on the development of a probabilistic empirical prediction system for forest fire risk on monthly to seasonal timescales across the Northern Hemisphere, using local and large scale climate information as predictors for future fire weather. The fire risk is quantified by the monthly drought code (MDC), which is an established indicator for seasonal fire activity. The forecasts are disseminated through the KNMI climate explorer, using an interactive online Python application, in order to convey forecast information in a simple and digestible manner. A forecasting page allows for end-users to assess local seasonal fire weather risk, associated forecast skill, and the relation between historical MDC and observed fires. The forecasts are updated monthly throughout the fire season. A research page allows for local and global analysis of the sources of predictability, and characterization of the patterns of spatial and temporal variability of fire weather risk.
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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.220 | 0.111 |
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".