An Analytical Study of the Effect of Heat Waves on the Forest Cover in Latakia Region in Syria: A Case Study of the Damage and Forest Recovery Rate in the Al-Kurdaha and Riseeun Forests During the Period 1975-2022
Bibliographic record
Abstract
This study aims to analyze the dynamics of heat waves in Syrian coast during the late twentieth and early twenty-first century, as well as their impact on forest fires in the early twenty-first century. The study also aims to determine the general trend of this phenomenon by analyzing the time series data of the Daily Maximum Heat Index at the climatic stations (Latakia, Qastal Moaf, Slunfah) from 1975 to 2022. Statistical methods such as linear regression and Spearman’s correlation were used to analyze the deviation of daily temperature rates during the study period from the overall trend of the period. Multiple linear regression models were employed to understand the complex effect of heat wave characteristics on forest fires on the Syrian coast. The findings revealed a statistically significant increasing trend in the number of days with heat waves. The general trend of heat waves showed a significant increase at the designated climate stations, with a confidence level of 95%. To assess the impact of thermal anomalies in the study area, the forest fire model in Latakia was examined in 2020 and 2016, taking into account dynamic weather changes. Landsat (30m) satellite images with medium spatial resolution were utilized to derive Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Moisture Index (NDMI). These analyses aimed to identify the key meteorological factors influencing the forest fire risk in 2020 and 2016, the relationship between the occurrence of fires and increasing heat waves in the study area, as well as the influence of heat waves on the burned area in Al-Kurdaha and Riseeun regions. The linear correlation coefficient (Pearson) was used to study the relationship between the number of heat waves and the number and area of fires in Latakia region. The results demonstrated a strong, positive, and significant correlation between the fire hazard index and heat waves, indicating an increased fire risk with rising heat waves in the study area.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".