Rubber Dams in Bangladesh Harness Surface Water for Farmers to Irrigate at Lesser Cost
Bibliographic record
Abstract
Farmers in Bangladesh have significantly extended agricultural activities into the dry winter-summer season over the past 2 decades to produce more through double and triple cropping and also to remain safe from damages due to floods. Agriculture in winter-summer season is fully irrigated. Ground water is now meeting the lion's share of this irrigation need. However, limit of abstraction of ground water within the capacity of technical and financial management by individual farmers, as it is now, will soon be reached at. It is, therefore, necessary that available surface water be harnessed, as far as possible, to support irrigation need in the dry season. Further to that, detection of arsenic contamination of ground water in some parts of the country in the recent past has made conservation of surface water all the more important. Also, surface water is cheaper and of better quality and so farmers have definite priority of choice of surface water over ground water for irrigation. There is no scope to develop reservoirs for storage of water in this flat country. With this background, Bangladesh has adopted Rubber Dams for conservation of water in the channels of its small and medium rivers to support winter-summer irrigation. Since introduction of the technology in 1995, Rubber Dams have been recognized as a successful method of water conservation under the conditions in Bangladesh. The Paper presents the technology of Rubber Dams in some details together with last 7 years' experience of Bangladesh with Rubber Dams. References have been made of the impact of Rubber Dam Projects on rural poverty reduction through the impact on agriculture of small and marginal farmers and through the scope of certain other economic activities for the local poor. Reference has also been made on the potential of application of Rubber Dams in regional countries — India and Sri Lanka together with interests indicated by some concerned agencies.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".