Export performance of fish (Fresh or chilled) from Sri Lanka
Bibliographic record
Abstract
The study attempted to investigate the Export Performance of Fish (Fresh or Chilled) from Sri Lanka. The study utilized secondary data from various sources, including export performance reports of the Export Development Board; Ministry of Fisheries, Aquatic and Ocean Resources; and TRADEMAP. The two parameters relative market share and market growth rate were used to classify the export market. It was found that the major importers of Sri Lanka’s fish (fresh or chilled) are France, Israel, the United States of America, Italy, Canada, Germany, and Belgium. France was the leading importer in 2021; approximately 28% of the fish exports went to France. Over the last three years, from 2019-21 the growth rate of market share increased in increasing rates for Denmark, Poland, Portugal, and Belgium. To ensure a competitive advantage in the world market, Sri Lanka should emphasize reducing post-harvest loss and improving quality standards. To improve the quality and marketability of its fish exports, a country should foster engagement with regional and global events. Policymakers should focus on fishery policies aligned with export market dynamics. These actions can improve branding, expand markets, and increase Sri Lanka’s fish export competitiveness internationally.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".