Economic Significance Of The Wetlands In The Malda District: Case Study Saili Beel In English Bazar Block, Kalma Beel In Chanchal Block-I And Bogole Beel In Chanchal Block-II Of Malda District In West Bengal
Bibliographic record
Abstract
Wetlands contribute to the national and local economies by producing resources and providing other natural benefits of the wetland. In West Bengal, the estimated benefit from natural wetlands cultivation is Rs. 3.59 lakh per year. The cost of irrigation from wetlands are cheaper for upland paddy cultivation as compared to irrigation from wetlands. Wetlands provide resources for local people such as food, water, raw materials for building in the village and raw materials for cottage handicrafts. Moreover, in West Bengal, wetlands are used for multiple purposes and have economic significant role in the livelihoods of the local people. It is also widely recognized that wetland have a significant influence on hydrological cycle. Wetland has therefore become important elements in water management policy at nation wetlands reduce floods, recharge groundwater augment low flows. Wetlands are important sources of aquatic biodiversity. This research paper is an attempt to highlight through observation and case study saili beel in English bazar block, kalma beel in Chanchal block-I and Bogole beel in Chanchal block-II of Malda district in West Bengal. The present study is focused on economic significance of different wetlands of the Malda district in West Bengal, India.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".