Marine Fishery Resources of Andhra Pradesh
Bibliographic record
Abstract
Andhra Pradesh with a coastline of 974 km encompassing 9 coastal districts has had a long \nhistory of fishing. Starting with traditional fishing in ancient times to the modern, technology-intensive \nfishing, the marine fisheries sector of the state has grown tremendously reaching record landings of \n3.42 lakh tonnes in 2014. The state has 555 marine fishing villages with 353 marine fish landing centres \n(CMFRI Marine Fisheriers Census, 2010). There are two major fishing harbors at Visakhapatnam and \nKakinada where bulk of total trawl catch (nearly 70%) is landed and three minor fishing harbors at \nBhairavapalem, Machilipatnam and Nizamapatnam. The marine fishermen population of the state is \nmore than 6 lakhs with roughly a quarter of them, being acive in fishery related activities throughout the \nyear. There are 31,741 fishing crafts in the marine fisheries of Andhra pradesh (CMFRI Marine Fisheriers \nCensus, 2010). The marine fisheries sector, at present, is an important source of employment and \nincome generation in the state, but is plagued with several problems. As because, this sector is vulnerable \nto external influences viz., overexploitation of marine resources, environmental degradation and climate \nchange, efficient management is the need of the hour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".