Analytical Study of the Temporal and Spatial Distribution of Fires in Lattakia Region, Syria in the light of the Current Climatic Changes
Bibliographic record
Abstract
This research aims to analyze the climatic changes Lattakia region, Syria and determine general trend of this change by analyzing the time series of the temperature and precipitation in Lattakia during the period (1960–2021), by applying the Mann-Kendall equation, It was found through the analysis of climatic data; The general trend of maximum and minimum temperatures is a significant (P < 0.05) increasing trend in the selected climatic stations, as it was found that the general trend of precipitation was around the average with a confidence level 95%. Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI)) were derived from satellite images. The calculated spectral indicators showed an increase in the fire risk during the studied period in conjunction with a clear decrease in the NDMI and NDVI indicators. The linear correlation coefficient (pearson) was also analyzed between the calculated Normalized Burn Ratio index and temperatures (dry, maximum, and minimum) and precipitation in the studied regions. The results showed a strong correlation between the Normalized Burn Ratio index and the precipitation, and this correlation was inversely and significant; Which indicates an increase in the fire risk with the increasing drought conditions in study region. Linear regression analysis was also used; Which highlighted the most important role of the minimum temperature in the fires spread in Lattakia region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".