Decadal Climate and Landform Variables Analysis in Iraq Using Remote Sensing Datasets
Why this work is in the frame
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Bibliographic record
Abstract
Iraq has experienced record-breaking temperatures, making it one of the hottest places on Earth. It is also ranked among the world's top five most climate-vulnerable nations. Climate change is a hazard to Iraq's people and may cause societal disintegration, instability, and displacement. Therefore, it is important to assess Iraq's decadal climate and landform variables analysis. In the present study, the Climate Hazards Center InfraRed Precipitation with Station (CHIRPS) data in the Google Earth Engine (GEE) platform from 2000 to 2022, as well as rainfall, anomaly, temperature, vegetation, and water, are used to analyse climate change in Iraq. As the land surface temperature (LST) rose by 2.63 °C, the data show that rainfall dropped by 61.45 mm in just 22 years of observation and by 2.79 mm yearly. Additionally, some urban expansion and climatic change have reduced the areas of water bodies and vegetation. The correlation matrix shows a higher negative association between the vegetation and LST indices, with R2 values of -0.58 (2022), -0.56 (2006), -0.60 (2012), -0.55 (2016), and -0.59 (2000), respectively. Iraq, extremely sensitive to climate change, is implementing several adaptation measures, including early warning systems, reforestation and mangrove planting, water management, a national adaptation plan (NAP), and a reforestation program. Due to vulnerabilities in vital areas including water, agriculture, health, and natural resources, Iraq is prioritizing adaptation to climate change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| 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) | 0.000 | 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 it