Sustainable Upcycling of Fisheries and Aquaculture Wastes Using Fish-Derived Cold-Adapted Proteases
Bibliographic record
Abstract
The fisheries and aquaculture industries are some of the major economic sectors in the world. However, these industries generate significant amounts of wastes that need to be properly managed to avoid serious health and environmental issues. Recent advances in marine waste valorization indicate that fish waste biomass represents an abundant source of high-value biomolecules including enzymes, functional proteins, bioactive peptides, and omega-3 rich oils. Enzyme-assisted processes, for the recovery of these value-added biomolecules, have gained interest over chemical-based processes due to their cost-effectiveness as well as their green and eco-friendly aspects. Currently, the majority of commercially available proteases that are used to recover value-added compounds from fisheries and aquaculture wastes are mesophilic and/or thermophilic that require significant energy input and can lead to unfavorable reactions (i.e., oxidation). Cold-adapted proteases extracted from cold-water fish species, on the other hand, are active at low temperatures but unstable at higher temperatures which makes them interesting from both environmental and economic points of view by upcycling fish waste as well as by offering substantial energy savings. This review provides a general overview of cold-adapted proteolytic enzymes from cold-water fish species and highlights the opportunities they offer in the valorization of fisheries and aquaculture wastes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".