Potential Aquaculture Practices In Saline Waterlogged Land Using Geospatial Approach Rohtak District (Haryana)
Bibliographic record
Abstract
For farmers whose agricultural land had been waterlogged and therefore unusable for years, there is some good news. Currently, the state administration is working to turn the flooded fields into ponds that can be used for fish aquaculture. In order to replace the "Green Revolution" with the "Blue Revolution,” The study focuses on the waterlogging issues and aquaculture potential in Rohtak district, Haryana, India, utilizing spatial and non-spatial data. Rohtak district spans 1,745 square kilometers, representing 3.9% of Haryana's total area. With average annual rainfall of 592 mm, the region experiences significant waterlogging, especially during the southwest monsoon season. Landsat-8 satellite imagery from 2022 was analyzed using unsupervised classification and the Normalized Difference Water Index (NDWI) to identify waterlogged areas. The depth of the water table and salinity levels (EC values) were also assessed using groundwater data. Results indicate that 42,229.4 hectares are severely waterlogged, while 82,693.8 hectares have salinity levels slightly unfavorable for aquaculture. Based on water depth, 12,061.7 hectares have excellent aquaculture potential, whereas 54.1% of the district is only slightly suitable for aquaculture. This study provides insights into water management challenges and opportunities for aquaculture development in the district.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".