Assessment of fishing gear efficiency, species diversity, and socioeconomic impacts on fishermen along the Jamuna River, Bangladesh
Bibliographic record
Abstract
The Jamuna River in Bangladesh is a vital source of freshwater aquatic resources and a natural breeding habitat for freshwater fish. However, recent declines in gear efficiency and species variety have significantly impacted the economic situation of fishermen. A study was conducted on 117 fishermen in Sariakandi upazilla, Bogra district, Bangladesh, from July 2022 to June 2023. The data was collected through direct visits, group discussions, and in-person interviews. The study identified 18 fishing gears and found that the seine net was the most effective gear for 8 months, with Cypriniformes being the most dominant fish order. The highest species abundance was recorded in September, while the lowest was in February. Only two types of fishing crafts were recorded during this period. The socioeconomic conditions of the fishermen were documented, with 71% being professionals and the rest being occasional. Most were over 40 years old, experienced, and had a large family. Their homes were kacha dwellings, with only 6% missing sanitary facilities and 76% using kacha latrines. Annual earnings ranged from 12500 to 169000 Tk, with 41% relying primarily on fishing and the remainder on other activities. A total of 59% of the fishermen borrowed loans from non-governmental organizations. This study shows that diminishing species diversity and total catch significantly impact fishermen's socioeconomic conditions. The government should take proper steps to protect the natural ecosystem of the Jamuna River and enhance the living needs of fishing communities in the study area.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".