Market Efficiency Indicators in Marine Fish Marketing in Goa, India
Bibliographic record
Abstract
The Indian State of Goa has a coastal length of 104 Kms and the State contributes 1.85% to the marine fish production of the country. A study was conducted to assess the market efficiency indicators such as Gross Marketing Margin, Percentage Share of Fisherman in the Consumers Rupee (PSFCR) and the Coefficient of variation. The study revealed that high value fishes such as cobia, silver Whiting, seer fishes, prawns and milk shark recorded a comparatively higher price spread. Varieties which recorded higher PSFCR were speckled prawn (72.86%), cobia (70.31%), seerfish (69.98%), Brown shrimps or ginga prawns (69.43%), pony fish (67.58%) and milk shark (65.61%). At the point of first sales, high value fishes such as cobia, seerfishes, prawns and silver biddy had a co-efficient of variation of less than 10% indicating a higher price stability. High value fishes such as ribbon fishes, seerfishes, cobia, indian white prawn, barracudas, brown prawns, speckled prawns, kadal shrimps and half beaks were among the list of fishes which recorded a low co-efficient of variation of less than 10% at the point of last sales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".