Climate change adaptations of shrimp farmers: a case study from southwest coastal Bangladesh
Bibliographic record
Abstract
Sustainability of brackishwater shrimp farming is of paramount importance for socio-economic development of southwest coastal Bangladesh. Here, shrimp farming is predominantly traditional, which is more vulnerable to climate change. Lack of understanding exists regarding the adaptation measures of local shrimp farmers in response to emerging impacts of changing climatic variables. This study provides the perceptions and adaptations of shrimp farmers in changing climatic context. A systematic random sampling method was employed to conduct a total of 240 questionnaire surveys and 60 key informant interviews from six sub-districts (Upazila) of southwest coastal Bangladesh to collect primary data. Changes in climate variables largely affect the shrimp yield by increasing frequency of shrimp disease, causing physical damage to farm structure and deteriorating quality of water. Shrimp farmers try to adapt to those changes in various ways, including increasing pond depth, exchanging tidal water, providing shade using aquatic plants, strengthening earthen dike and netting and fencing around the dike. Shrimp mixed cultivation is the most popular form of shrimp farming in the study area. More emphasis on implementing polyculture shrimp farming is necessary to improve climate change adaptation and to promote sustainability of this aquaculture practice in southwest coastal Bangladesh.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".