Predicting long-term climate changes in Iraq
Bibliographic record
Abstract
Abstract Changes in air temperature have a significant impact in Iraq due to global climate change. The objective of this study is to project future trends of air temperature in Iraq. In this study, the future air temperature was projected for 2025, 2050, 2075 and 2100 from the CCSM4 climate model belong to CMIP5 under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenario for Iraq. The historical observed air temperature data (1950 – 2014) acted as referenced as the mean air temperature data obtained from 18 meteorological stations. Statistical downscaling has implemented. The model outputs were calibrated by using around 80% of the observed historical and model historical data. After that, it proved a significant performance of a statistical downscaling process for simulation air temperature for future periods. The results revealed that the mean air temperature would increase under the four RCPs scenarios with different levels. The lower increase rate belongs to the RCP2.6 scenario, the increase rate is expected to be (0.5-0.8 °C) above the observed historical level. However, the RCP8.5 has the highest rate at (4.1 -6 °C) while, the RCP4.5 and RCP6.5 have (1-2 °C) and (2-4 °C) respectively. On the other hand, the temperature expands direction is from the south toward central, west and north of Iraq.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".