Investigation over a national meteorological fire danger approach for Turkey with geographic information systems
Bibliographic record
Abstract
The aim of this study was to investigate Meteorological Fire Danger Indices for Turkey. A number of internationally implemented fire danger indices were calculated with Fire Danger Processing software and their performances were tested with Mandallaz and Ye’s Performance Score Method. As a result, among other meteorological fire danger indices that have been applied by several fire fighting administrations and services, the U.S. National Fire Danger Rating System, Mc.Arthur’s Fuel Moisture Model and Forest Fire Weather Index, BEHAVE Fine Fuel Moisture Model and Keetch Byram Drought Index, the Canadian Fire Weather Index was selected as the best performing fire danger index for Turkey. Calibrated with monthly fire history data of the last 5 years’ records, the results during the determined fire season were integrated with vegetation cover data for Turkey, derived from GLC 2000 global land cover data. Besides, daily performance of the Canadian Fire Weather Index was observed by three consecutive days in August 2006 and the outcomes were evaluated with the information about fire events compiled from newspaper archives. The study is a first attempt for further fire related analysis at the national scale; an attempt to establish an early warning system and a spatial base for mitigation effort for the wild fire phenomenon in Turkey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".