Current freezing and thawing scenarios employed by North Atlantic fisheries: their potential role in Newfoundland and Labrador’s northern cod ( <i>Gadus morhua</i> ) fishery
Bibliographic record
Abstract
Seafood is very perishable and can quickly spoil due to three mechanisms: autolysis, microbial degradation, and oxidation. Primary commercial sectors within the North Atlantic fisheries include demersal, pelagic, and shellfish fisheries. The preservation techniques employed across each sector can be relatively consistent; however, some key differences exist across species and regions to maintain product freshness. Freezing has long been employed as a preservation technique to maintain product quality for extended periods. Freezing allows seafood to be held until demand improves and shipped long distances using lower-cost ground transportation while maintaining organoleptic properties and product quality. Thawing is the opposite of freezing and can be applied before additional processing or the final sale point. However, all preservation techniques have limitations, and a properly frozen and thawed fish will still suffer from drip loss. This review summarizes the general introduction of spoilage and seafood spoilage mechanisms and the latest preservation techniques in the seafood industry, focusing on freezing and thawing processes and technologies. This review also considers the concept of global value chains (GVC) and the points to freeze and thaw seafood along the GVC to improve its quality with the intention of helping Newfoundland and Labrador’s emerging Northern cod ( Gadus morhua ) fisheries enhance product quality, meet market demands and increase stakeholder value.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".