Delineation of Water Logging and Salinity for Salvaging Built Environment
Bibliographic record
Abstract
Millions of acres of splendidly productive land and valuable infrastructure are deteriorated continuously. The reason for such deterioration in majority of areas is mainly due to water logging and salinity. Rise of water table level and the dearth of drainage and lack of continuous monitoring and timely remedial measures, extended the circle of devastation to historical heritage and precious archeological sites as well. Mohenjo-Daro has been selected for this study, it has global significance but due to water logging and salinity, it is in danger of total destruction. The archeological buildings and other infrastructure and land in its environs are being gradually eroded by the capillary rise of saline ground the intensity of which constitutes a serious threat. For delineating and periodic monitoring of the salinity and waterlogging and to effectively implement the appropriate remedies, use of the latest technologies is essential. In this study the remote sensing technologies are used to address this issue with the help of Soil investigation parameters mainly EC and pH. The aftermaths of this study would provide a methodological framework along with practical application in delineation saline areas using satellite technology. The final value-added products of this research would be useful for all interested stakeholders including conservationists, environmentalists, archeologists, planners and decision-makers at various levels. The international community at large would be the beneficiary of this study since Mohenjo-Daro is the heritage of entire mankind.
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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 | 0.001 |
| 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".