Expansion of Residential Colonies and Conversion of Farmland in Bahawalpur City, Pakistan: A Temporal View
Bibliographic record
Abstract
This study focuses on the issue of farmland conversion into housing colonies in Bahawalpur City. In order to understand the magnitude of this issue, data of 102 sample colonies was collected for the period of 1950-2011 through field survey and secondary sources including TMA Bahawalpur City. The year of establishment, area occupied and legal status of colonies were recorded and the data were aggregated into 10-year categories for analysis and to produce temporal maps. Results indicate that during the last 61 years, an area of 1,142 acres (462.15 hectares) had been converted to 102 colonies at an average rate of 18.72 acres per year. Among these colonies only 18 were approved by concerned authorities whereas 84 were not approved legally. Approved colonies occupy an area of just 197 acres (17.25% of the total) whereas non-approved colonies and towns cover a huge area of 945 acres (82.74%). The conversion of farmland fluctuated substantially over time. During the period of 1950-1960 merely seven colonies were built which consumed an area of 97 acres indicating a conversion rate of 9.7 acres per year, while during the period of 2000-2010 a total of 32 colonies were built that consumed an area of 422 acres (170.77 hectares) indicating conversion rate of 42.2 acres per year. These findings indicate that the rate of farmland conversion is accelerating. If this trend goes unchecked, the problem of farmland conversion may change into a serious threat for future food supplies. This study identified several suggestions to tackle the issue.
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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 | 0.002 | 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 | 0.001 |
| 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".