Geo Spatial Assessment of Flood Hazard in Jhang District, Pakistan
Bibliographic record
Abstract
Floods are one of the leading natural disasters in Pakistan. In recent decades the frequency and intensity of floods has increased due to human and environmental factors. In this work an attempt was made to examine the flood vulnerable areas of Jhang district and its impact on land cover changes by using remote sensing and GIS techniques. Image Analysis and different indices like NDVI, NDWI were applied on satellite images for identification of flood prone areas and vegetation conditions in study area. Flood prone areas were further classified into low, medium and high risk areas according to flood hazard. The results indicate that, Jhang covers total area of 6357 km2 out of which flood affected area is 530 km2. During the flood of 2010 water area expanded approximately 714 km2 while a decline was observed in agricultural lands which have lost139 km2 area under vegetation
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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 | 0.001 | 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".