Bibliographic record
Abstract
Fisheries sector plays a significant role in the Indian economy in terms of its contribution to growth and development. The contribution of fisheries sector to Indian merchandise trade and to world fishery trade is substantial.. Fish production has increased from 7.52 lakh tonnes (5.34 lakh tonnes for marine and 2.18 lakh tonnes for inland fisheries) in the year 1950–51 to 114.09 lakh tonnes (36.41 lakh tonnes for marine and 77.69 lakh tonnes for inland fisheries) in 2016–17. The growth sector of fish sector contributes through its share in GDP and foreign exchange earnings gained through the export of fish and fishery products. During 2016–17, the volume of fish and fishery products exported from India was 134948 tonnes worth Rs.378709.0 crores. The export mainly consist of frozen shrimp verities (41.10%) followed by fin fish (25.64%), frozen squid (7.32%), and frozen cattle fish (5.02%), during 2017–18. The paper tries to see the revealed comparative advantage of India of this sector with its Competitors and the findings show that India's export potential for fishery product have continuously improved in the study period and the fishery sector has strong comparative advantage in terms of total world's fishery exports. India is more advantageously placed than Canada, Chile, Russian Federation, Spain, Sweden and United States of America. The analysis suggests that India is comparatively in an advantageous position compared to its competitors.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".