Price stability of commercially traded fishes in ernakulam markets, Kerala
Bibliographic record
Abstract
The fish consumption of Kerala is four times the national average, as 85 per cent of the Keralites eat fish, which accounts for over three quarter of the animal protein intake of an average Keralite. Though the fish demand-supply gap is ironed up by the arrivals from the neighboring states and even from the fish landing centres further north, huge fluctuations are visible in the retail prices of fish. In this backdrop, the present study was conducted at Ernakulam district, the commercial capital of the state, which abodes around 2.1 million metropolitan population with an average monthly fish consumption of 9.34 kg. The study analyzed the trends in price volatility of the major commercially traded fishes in selected Ernakulam markets, identified the species which exhibit stability in prices and also deduced the relationship between marine landings vis-à-vis price realized in the district. The study revealed that high value species are found to be having better stable prices compared to the low value species. Also, the retail prices are found independent over the quantity landed in the district.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".