Efficiency and Capacity Utilization of India’s Marine Fisheries
Bibliographic record
Abstract
For decades, the problems of excess capacity and overfishing have been the subject of considerable attentions, sincethey are the primary reasons for the depletion of fish stocks, reduction of the profitability and economic performanceof the fishery sectors at the national and international levels. As a result, estimations of technical efficiency,harvesting capacity, and capacity utilization has become an increasingly important practice in the fishery, since theyprovide useful information about the optimum allocation of inputs and outputs, and guide policy formulation tocombat biological and economic losses. Based on the Johansen (1968) definition of capacity we have examined thetechnical efficiency, capacity and capacity utilization of the marine fishery sectors of the India’s 9 marine states and4 union territories using an output oriented data envelopment analysis approach. The result of the study shows thatmajority of the states/union territories have been inefficient and have the capacity to harvest considerably more thanwhat they have actually been harvesting by using the existing resources in an efficient configuration and showed howserious the problem of excess capacity is in the India’s marine fishery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".