Overview of elasmobranch fisheries of West \nBengal in 2018
Bibliographic record
Abstract
Elasmobranch fishery of West Bengal comprises of sharks, rays, guitarfishes and skates. Due to demand in the national and international market, the fishery has gained importance though it is not a targeted resource. The \ncatch data showed that the fishery is in a declining phase since 2016. The estimated landing of elasmobranchs \n(3799 tonnes) has shown a further decrease of 12.6% during 2018 in West Bengal compared to 2017. Sharks form the major portion (48%) of the elasmobranch fishery followed by rays (40%) and guitarfishes (12%) during \n2108 in West Bengal. The fishery flourished more during the first (January-March) and last quarter (October- \nDecember)of the year. Maximum catch of sharks have been observed in October followed by February. The \ngear-wise landings of sharks showed that multiday trawlers contributed 81% of the shark landings followed \nby mechanized gill netters (17%) and the remaining 2% by inboard gill netters. Maximum catch of rays have \nbeen observed during June followed by January and October. The rays were mainly exploited by trawlers (76%) followed by hook and lines (15%) and gill netters (8%). Maximum catch of guitarfishes was observed during \nJanuary followed by August and February. Guitarfishes are landed mostly by trawlers (91%) followed by gill nets (9%). The elasmobranch resources in West Bengal are very diverse in nature. However, there is a continuous \ndecline in the landings which could be detrimental in future if the resources are not managed properly. Hence, it is recommended to follow good management practices to ensure long term sustainability of the resources.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".