DHAKA WATER SUPPLY SYSTEM MANAGEMENT DEVELOPS A MODEL OF URBAN DRAINAGE OPRATION AND MAINTENANCE PROJECT WITH SOFTWARE TECHNOLOGY
Bibliographic record
Abstract
Dhaka Water Supply System Management (i.e.; Dhaka WASA) is the main service provider in drainage system maintenance in Dhaka city. In the mega-city of Dhaka the capital of Bangladesh, with over 10 million people facing acute problems in increasing urban flooding, storm sewrage and sanitation which affecting millions of inhabitants and businesses every year. To remedy these urban flooding problems the responsible authority, (DWASA) has started the Urban Dredging Demonstration Project (UDDP) under the existing partnership with Vitens Evides International from the Netherlands. Once Dhaka city has 49 canals which served as the natural drainage system but with the course of time most of canals are illegally occupied and disappeared and rest of them have become narrower, silted up, and blocked. To overcome this problems adoption of new dredging tecnology and longterm dredging plan with WIT (sediment information system) offers a solution. WIT is a dynamic web-based GIS application for planning and maintenance operations of drainage system. It offers a unique combination of tools which makes it possible to process data and prepare long-term dredging plans. Practical modules allow for the optimisation of maintenance dredging by calculating quantities dredged material, the testing of data quality and planning of maintenance dredging. The data can be visualized in tables, diagrams, charts for invsetgating the following questions: location of dredged waterways, volume of sediment (material) to be dredged, quality (chemical and physical) of the dredged materials, total costs of the dredging operations and maintenance, available sites for disposal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".