Evaluating Water Scarcity Indices for Cultivation Region in Sadat Al-Hindya, Babylon, Iraq: A Case Study
Bibliographic record
Abstract
The study evaluated the Water Scarcity Indices for Cultivation Region in Sadat Al-Hindya, Babylon, Iraq. It calculated the reference evapotranspiration, actual evapotranspiration, and amount of precipitation with effective rainfall to estimate the droughts indicators which are the Standard Precipitation Index (SPI), the Standard Precipitation and Evaporation Rain Index (SPEI) and Reconnaissance Drought Index RDI. The study indicated that the greatest decrease in river flow occurred from 2019-2021 to 2020-2021 due to increasing temperature in summer and decreasing precipitation in winter. This research evaluated a wet and drought indicating for planning and management of water resources to face changes in climate of future. The research showed the last years were years of drought according to the three indicators. SPI ranged from 0.5 to 1.5 in the rainy years, but it was -0.5 to -1 as moderately dry because in the middle of Iraq while in the south of Iraq was severely dry or extremely dry. SPEI of the study area ranged from -1.5 to -2.5 which means severely dry. The SPEI measures are negative values meaning the months and years were drier. RDi ranged from 0 to -1 was dry and moderately dry while some months and years are positive and will be wet through rainfall for ten years (2014-2023). From indices showed that the region was a drying study area due to the impact of climate change because of the reduction of precipitation and increase in temperature which caused a rise of evapotranspiration during the last few years.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".