Extreme Climate Events and Fish Production in Bangladesh
Bibliographic record
Abstract
Natural hazards frequently batter Bangladesh and cause damages to fisheries sector of the country. The main objective of the present investigation was to evaluate the effects of storm/tidal surge, waterlogging, cyclone, flood, drought and erosion on spatial distribution of damages and economic loss in fisheries of Bangladesh. Data were collected from existing literatures followed by scoring and attribute-wise maps were prepared using IDRISI3.2. The highest economic loss (US$ 17.65 million) in fishery sector was observed in Southern part and the least in hilly regions. The damages caused by natural hazards followed the order of storm/tidal surge > waterlogging > cyclone > flood > drought > erosion. About 21% areas of South and South-east Bangladesh were affected by high to very high storm/tidal surge. Very severe waterlogging problems were observed in 6.96% areas of the country. Moderate to high damages because of cyclone were found in about 11% areas in South and South-east Bangladesh. Moderate to high flooding problems were mostly prevalent in Central and North-east part of the country covering 15-19 per cent areas. Drought and erosion are less damaging to fishery sector compared to other studied natural hazards. Although exposure index to natural hazards is high, relative index to national economy because of damages to fisheries sector are low. Adaptive measures in coastal areas as a long-term strategy would be participatory construction of hard structures and reclamation/conservation of wetlands throughout the country including improved warning system could be undertaken for minimizing damages in fisheries sector of 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".