Bibliographic record
Abstract
Since the last century, Iraq has suffered from a shortage of water needed for irrigation and other essential uses for human existence.This water scarcity has affected the Tigris-Euphrates River basin, resulting in unusual variations in public activities in watershed areas due to rapid population growth and urban expansion.Therefore, the aim of this study is to evaluate and detect changes in Land Use/Land Cover (LULC) in the main regions of the Euphrates basin in central Iraq over a time scale from 1985 to 2022, focusing on the availability of built-up lands, vegetation, barren areas, and their relationship with water bodies.The changes in LULC have been studied based on a database and satellite data, indicating high-resolution geo-registered images supported by interpretation keys to detect the changes that have occurred over time.The detection process has been accomplished using Remote Sensing and ArcGIS V.9.3 software.Landsat TM and ETM+ satellite images from 1985, 1999, and 2022 were used to monitor Land Use classes to identify the drivers of this change.The results have shown that high-resolution satellite images, Remote Sensing, and GIS techniques are powerful analytical tools for conducting LULC change detection analysis.They classify the geographical location and type of changes, quantify the changes, and evaluate the accuracy of change detection.The information on LULC change detection will be helpful to experts and urban planners for improving future plans for the sustainable development of regional lands and cities, as it is one of the main forces driving global environmental change and sustainable improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.745 | 0.709 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".