Decadal Climate and Landform Variables Analysis in Iraq Using Remote Sensing Datasets
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".