Hydrological assessment for the availability of water for off-stream uses of Karatoa-Atrai River in Bangladesh
Bibliographic record
Abstract
Abstract Explicit consideration of in-stream flow requirement (IFR) has now become almost mandatory in many rivers before irrigation withdrawal is made. Thereby, the primary objective was to evaluate the IFR through hydrological approaches and compare the condition with current flow variability and trends. Flow records were collected from five discharge stations for IFR estimation. Performance of the river was also judged with respect to nine hydraulic cross-sectional data and stage data of ten water-level monitoring stations of Bangladesh Water Development Board (BWDB). Results show that since 2000, the upper Karatoa was able to meet IFR, but the lowest part of the river experienced severe deterioration in addressing its dry season functionality. Also, the decreasing trend in off-stream availability is recognized as a threat to the Singra site resulting from severe aggradations of the river beds. Attention to the less off-stream availability at Singra raises concerns over sustaining the river from drying out. It is now evident that a hydrological approach of IFR is more than just an initial rough estimate. Such a precautionary method works well to provide quick technical support and decision reference for a complex system, in particular, to find specific drying out parts of a river of concern.
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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 | 0.000 |
| 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 | 0.000 |
| 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".