Suggested ways for improving the management of the Bay of Bengal shrimp trawl fisheries:\nA literature study on challenges and bycatch mitigation in shrimp trawl fisheries around the world and examples on how it is implemented in the management regimes in various nations.
Bibliographic record
Abstract
India, Sri Lanka and Bangladesh have border with Bay of Bengal. Sri Lanka and India has good consciousness on bycatch situations in their part. But Bangladesh has very little intension on bycatch strategies. This paper has main intention to write about Bangladeshi part of Bay of Bengal on bycatch situation. \nAs a littoral state of the Bay of Bengal, Bangladesh has ample coastal and marine resources. Bangladesh has marine fisheries resources with 475 species of fish, 36 shrimp, 5 lobsters, 12 crabs and 33 sea cucumbers and many other aquatic fauna and flora. \nThough there are several rules and regulation are already existing by the marine authority, but there is no bycatch and discards data and any preventive measures have taken by the Government of Bangladesh in The Bay of Bengal which is very important factor in marine resources.\nThis paper mainly focuses on improving current bycatch situation on the Bay of Bengal (Bangladesh Part). Here, mainly several literature reviews are done.\nThe paper has four parts; first part explains the overall bycatch situation on Bay of Bengal (Bangladesh Part) like fishing fleet, shrimp trawling, management regimes with mitigation procedures taken by the Bay authority. Second part shows the explanation of bycatch and discards. Thirdly, it reveals current overall bycatch views and strategies taken by USA, Norway, Australia, Canada, South Africa. Fourthly, it shows some strategies which can be taken by current fishers and marine authority of Bangladesh to handle the bycatch for maintaining sustainability and earning healthy revenue based on some experienced countries who manages bycatch very adequately.\nRecently, Bangladesh get reasonable marine areas which makes more volume of its own areas. In this case, this time is very crucial for Bay authority in Bangladesh to take proper action in Bangladesh. \nPresently, Bangladesh is heading for being strong economy compare to earlier years. If the Marine authority could be more conscious to aware on bycatch on the Bay, marine sector must be very effective stand for the growing of national economy. \nIf we see the current technical and management measures what are practicing right now in Bangladesh still are very effective according to the environment and culture of fishers. But, present ongoing measures in successful nations over bycatch is also a important part for future outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".