A Decision Support System for Forest Fire Danger Notices in Ireland 
Bibliographic record
Abstract
Since 2006 the Canadian Forest Fire Weather Index System (FWI) has been employed operationally at Met Éireann to predict the risk of forest fires in Ireland. Around 11% or 770,000 ha of the total land area of Ireland is afforested, but there are also large areas of open mountain and peatlands covered in grasses, dwarf-shrub and larger woody shrub type vegetation which can provide fuel for spring wildfires under suitable conditions. After winter, vegetation can be dead or have a very low live moisture content, and the flammability of this vegetation can be readily influenced by prevailing weather, especially following prolonged dry periods.Different decision support tools are available to different sectors, namely:The General Public: who have access to fire weather index meteograms on Met Éireann’s public website. Local Authorities, who have access to the ANYWHERE multi-hazard warning system, which provides multiple sources of information about fire danger and propagation. The Department of Agriculture, Food and Marine (DAFM), who are provided with information and additional support from National and European partners and networks. DAFM is the Forest Protection authority in Ireland responsible for issuing Forest Fire Danger Notices which improve preparedness for fire responses and are based on a range of factors including information provided by Met Éireann who calculate the FWI and FWI components using observation data at synoptic stations, and the predicted FWI for the next five days ahead based on numerical weather prediction data. This allows fire responders to build resilience and prepare for impending fires.The FWI is determined based on the types of forest fuel and how quickly they dry out/get rewetted, and components of fire behaviour. The FWI represents the fire intensity as the rate of energy per unit length of fire front (kW/m). The components which provide the most accurate indication of risk under Irish conditions are the Fine Fuel Moisture Code and Initial Spread Index, based on the fuels involved and ignition patterns observed to date. Since 2022 Met Eireann provide the FWI as well as the individual components Fine Fuel Moisture Content and Initial Spread Index via the public website for synoptic stations. These indices are based on observations and a seven-day forecast into the future using ECMWF predictions. This allows all county councils responsible for wildfire preparedness to access this information swiftly and directly.Met Éireann also use the ANYWHERE multi-hazard warning tool which allows for visualisation of multiple fire-related risk factors and warning indices to be viewed simultaneously. The ANYWHERE system, in combination with our station-based forecast and antecedent conditions, provide fire managers and response teams with excellent information with which to make decisions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 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".