How Do Vulnerable People in Bangladesh Experience Environmental Stress From Sedimentation in the Haor Wetlands? An Exploratory Study
Bibliographic record
Abstract
Abstract The haor landscape is a wetland ecosystem in northeast Bangladesh, comprising shallow depressions that undergo large changes in water inundation between the monsoon and dry seasons. Sediment is supplied to the haor from rivers originating in the adjacent Shillong Plateau, and can adversely affect these largely arable agricultural lands. The critical adverse effects of changing hydrology and enhanced sedimentation due to anthropogenic change include the reduction of natural water storage capacity, changes in the timing and magnitude of floods, and increasing loss and damage to crops, which consequently affect the livelihoods of wetland dwellers. This study employs a mixed‐methods approach to investigate how sedimentation has affected the livelihoods of the residents and the pressures they experience. Cross‐sectional surveys and interviews were conducted with 180 respondents in Lubar and Pochashul Haors (LPH), Sunamganj District, and allied with an analysis of satellite images that reveals the nature of landscape change over the past 56 years. Our findings confirm that sedimentation has been promoted through both natural processes of alluvial fan progradation and anthropogenic forcings. Enhanced sedimentation threatens the agriculture of the region and affects the livelihood of local inhabitants, leading to other societal issues related to income, education, employment, health, displacement, and sexual harassment. The mixed methods employed herein are essential tools to reveal these effects. In order to reduce the vulnerability of the local population, a transboundary dialog between India and Bangladesh is needed to realize measures to protect wetland resources and achieve progress towards environmental sustainability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".