Renewable resources from the oceans: Adding value to the by-products of the aquaculture and fishing industries
Bibliographic record
Abstract
This paper presents an overview of this field of research, outlines some of the opportunities available to add value to fishery by-product streams alongside some of our most recent studies and data in this area. In Atlantic Canada around 418 000 tonnes per year waste is produced at fish processing plants. This waste could be used to give a range of products as it is made up of diverse substances including scrap meat, bones, shells and process water. Possible products include fish oils, gelatin, biopolymers and minerals. These could be used in a range of industries from food, medicine and biotechnology to mining, chemical, oil and gas sectors. Chitin is a biopolymer that makes up to 30% of the waste in crustacean (shrimp, lobster and crab) shells. We have been looking at new uses for it beyond its application as a biomedical material. Our results in this area will be described. Finfish processing plants produce a varied waste stream but an oil-rich product can be isolated and used as a heating oil. Blue mussel shells are a by-product of mussel farming/processing in Newfoundland and could facilitate the development of new material outputs. These shells are rich in calcium carbonate and have the potential to be transformed into adsorbents and catalyst supports.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".