Study of Land Cover Changes in the Center of the Holy City of Karbala During the First Quarter of the 21<sup>st</sup> Century
Bibliographic record
Abstract
Abstract This study provided an assessment of the reality of green spaces and the classification of land cover in the district of Al-Markaz, which is part of the holy city of Karbala “the study area”, This may assist in improving the environmental and urban reality within it. The changes in residential areas, vegetation cover, soil/barren land, and water bodies within the administrative boundaries of Al-Markaz were studied. Additionally, the distribution of green and urban spaces was classified, determining the Percentage of green space to the city’s total area in the study area to evaluate its development during the first quarter of the twenty-first century. The vegetation cover was demonstrated by integrating spectral bands: red, green, and infrared, to calculate their number, area, and distinguish between implemented and non-implemented areas.A supervised classification method was used to differentiate between various color grades of satellite images: 2000,2005,2010,2015 and 2024 from the satellites Sentinel-2A, Landsat 8-9 OLI, and Landsat 5 TM within the ArcMap 10.7 Geographic Information System environment, The study indicated that the largest area of green spaces was 25.62% of the total area of the study area in 2020, with the highest percentage of urban areas and soil/barren land being 39.74% and 41.39%, respectively, in 2024, which achieved the lowest percentage of green spaces over the past 24 years, reaching 18.21%. The actual per citizen share of implemented green spaces was only 0.84 m 2 .
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".