Fish processing wastes used as feed ingredient for animal feed and aquaculture feed
Bibliographic record
Abstract
Fish wastes management has become a global problem from the last years. Dispose of seafood wastes cause environmental pollution. To overcome this issue these unwanted seafood products are used for the formation of animal feed and aquaculture feed. These unwanted products include small fish and those parts of fish which are not used as human food. These unwanted parts include viscera, head, fins and skin of fish. These byproducts are rich source of protein, minerals and vitamins so these can be used as a supplement in animal feed. These are also used to fulfill the deficiency of protein in animals. These byproducts can be used in the form of fish meal, fish oil, and protein hydrolysates and fish silage. Protein hydrolysates provide high amount of nitrogen and fish oil provide triglycerides of fatty acids and phospholipids in the animal feed industry. These are also used in the formation of pet feed and in the formation of fertilizers. These byproducts are processed for feeding by using fermentation, biotechnological and bio preservation techniques. © 2020, Green Wave Publishing of Canada. All rights reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".