Urban Expansion on Agricultural Lands in Madaba District, Jordan During the Years 1984-2022
Bibliographic record
Abstract
The current study aimed to identify the effect of urban expansion on the agricultural lands in the District of Madaba Center during the period of 1984-2022 as well as the effect of the natural (topographical) and human (population growth) factors on defining the directions of urban growth and predicting the future of the agricultural and urban areas under population growth by using the Geographic Information Systems and Remote Sensing tools (ENVI). The study relied on the images obtained from Landsat Satellites (5,8 and 9) through the USGS website for the years 1984, 1994, 2005, 2014, and 2022; this was in addition the aerial photographs that were obtained from Google Earth Pro application, the maps, and the descriptive data that benefit the study subject. The study findings of urban expansion on the agricultural lands indicated an increase in the built-up areas with a percentage of 214.2% during the study period as well as a decrease in agricultural lands with a percentage of 31.17%. Moreover, the study revealed a variation in the percentages of spatial distribution concerning the types of land use among the district’s zones during the same period as a result of the natural and human conditions within each zone. Furthermore, the findings indicated that urban growth in the District of Madaba Center is moving from the southwest towards the northeast, influenced by the natural and human factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".