An assessment of target markets for Türkiye's sea bass (Dicentrarchus labrax) exports: The CAPMA technique approach
Bibliographic record
Abstract
Türkiye is the global leader in sea bass (Dicentrarchus labrax) production and ranks second in exports. While sea bass is Türkiye's most exported fish, it is crucial to enhance this export to provide greater economic value and ensure the sustainability of production and exports. Therefore, exploring alternative markets is essential. This study aims to evaluate, compare, and classify alternative markets with the potential to boost Türkiye's sea bass exports. For this purpose, a relatively new multi-criteria decision-making (MCDM) method, the Classification Approach of Potential Market Alternatives (CAPMA) technique, was used in the study. The study encompassed a comprehensive analysis of 30 alternative markets, evaluated against five criteria, and categorized into four groups based on their capacity to enhance export volumes. The analysis identified nine countries (Sweden, France, Poland, Hong Kong, Japan, China, Thailand, the United States (US), and Malaysia) with the highest potential to increase Turkish sea bass exports. Six countries (Germany, Denmark, Finland, Singapore, Brazil and Canada) were identified as other countries where export potential is important to ensure country diversification, which is important for Türkiye's sustainable sea bass production and exports.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".