Developing a Comprehensive "Boat to Dock" Traceability Framework for China's Coastal Fisheries
Bibliographic record
Abstract
The complexity of seafood supply chains, marked by numerous production and distribution nodes, poses significant challenges to achieving end-to-end traceability. Such traceability is essential for food safety, legal compliance, curbing illegal, unregulated, and unreported (IUU) fishing, and promoting sustainable practices. While seafood labeling regulations are well-established in regions such as Europe, the United States, Canada, and Australia, China currently relies solely on the General Rules for the Labelling of Pre-Packaged Foods (GB7718) established by the National Health Commission (NHC), with no mandatory traceability standards in place. This study aims to facilitate the establishment of a "boat to dock" fish catch traceability system for China's coastal waters by identifying gaps in current management practices, traceability mechanisms, and relevant regulations. Through a comprehensive literature review, analysis of existing standards, and stakeholder interviews, we identify specific challenges and benchmark best practices from both domestic and international contexts. Our proposed framework provides a roadmap for implementing a robust traceability system, with two key outcomes: 1) enhancing stakeholder awareness and capacity for traceability in fishing catches; 2) delivering actionable policy recommendations to support traceability standards in China's coastal fisheries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".