How Do Vulnerable People Adapt to the Impact of Sedimentation in the Haor Wetlands of Northeastern Bangladesh?
Bibliographic record
Abstract
Abstract The frequency and geographic extent of floods in northeastern Bangladesh have increased over the past few decades, and sedimentation has gradually raised the beds of wetland water bodies. The present study examined how households (HHs) cope with, and adapt to, the adverse effects of sedimentation in the haor wetlands under extreme weather conditions. Lubar and Pochashul haors (“LPHs”), in the Sunamganj District region and most affected by sedimentation, are the primary focus of this study. Questionnaire surveys from 180 HH respondents, transect walks, key informant interviews, and focus group discussions were conducted to gather data on adaptation strategies for counteracting wetland sedimentation. Descriptive statistics and qualitative data reveal that the residents of Bangladesh’s haor wetlands face difficulties due to flash floods and sedimentation. The study shows that residents borrow money and food, sell their possessions, and use other assistance-based resilience strategies. Food-based strategies, such as limiting the quantity and quality of meals, are commonly employed by these HHs in the short term. However, some long-term strategies followed by the residents are not viable, such as changing professions or increasing the use of pesticides in agriculture. The study also finds inventive and constructive ways of making improvements based on traditional knowledge and modifying the agricultural practices used by local people to combat sedimentation. In the event of flooding and sedimentation, our study reveals that wetland inhabitants may use counterproductive survival strategies based on outside innovation and their traditional knowledge, rather than destructive strategies such as reducing food consumption, changing jobs, and reducing the sale of resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".