Utilization of marine by-products for the recovery of value-added products
Bibliographic record
Abstract
The world fisheries resources have exceeded 160 million tons in recent years. However, every year a considerable amount of total catch is discarded as by-catch or as processing leftovers, and that includes trimmings, fins, frames, heads, skin, viscera and among others. In addition, a large quantity of processing by-products is accumulated as shells of crustaceans and shellfish from marine bioprocessing plants. Recognition of the limited marine resources and the increasing environmental pollution has emphasized the need for better utilization of the by-products. Marine by-products contain valuable protein and lipid fractions, minerals, enzymes as well as many other components. The major fraction of by-products are used for feed production—in making fish meal/oil, but this has low profitability. However, there are many ways in which the fish and shellfish waste could be better utilized, including the production of novel food ingredients, nutraceuticals, pharmaceuticals, biomedical materials, fine chemicals, and other value-added products. In recent times, much research is conducted in order to explore the possible uses of different by-products. This contribution primarily covers the characteristics and utilization of the main ingredients such as protein, lipid, chitin and its derivatives, enzymes, carotenoids, and minerals originating from marine by-products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".