Present Ecological Status of Beki River, and the World Bank’s interventions in the district of Barpeta, Assam
Bibliographic record
Abstract
The Beki River, a crucial tributary of the Brahmaputra in Assam, plays a significant ecological and socio-economic role in supporting the local communities through agriculture, fishing, and other livelihood activities. However, the river has been facing various environmental threats, such as pollution from agricultural runoff, erosion, and overfishing, which have led to deteriorating water quality and declining biodiversity. This study aims to assess the water quality of the Beki River, evaluate its biodiversity, and analyze the socio-economic dependencies of local communities on the river. A comprehensive methodology was employed, including water sampling, biodiversity surveys, and socio-economic assessments. The study identifies key pollutants, such as heavy metals and fertilizers, affecting water quality and highlights the impact of river erosion on local livelihoods. Biodiversity assessments reveal a decline in native fish populations, exacerbated by anthropogenic pressures. The findings emphasize the need for sustainable management strategies, including pollution control, erosion prevention, and community-based conservation efforts to protect the river’s ecosystem and support the local economy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".