Türkiye için ulusal bazda meteorolojik yangın tehlike indekslerinin coğrafi bilgi sistemleri ile araştırılması.
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".