Hemp processing by-products: a potential source of new aquafeed ingredients
Bibliographic record
Abstract
Aquaculture production activities occur in every province and territory of Canada, and in 2020 Aquaculture industry generated $3.86 Billion in economic activity and employed 14,520 full time workers in Canada. The salmonid aquaculture industry has largely transitioned to using plant-based proteins and oils to reduce reliance on traditional marine ingredients (fish meals and oils), which have reached their limits. Exploring new ingredients for salmon and trout aquafeeds is crucial to meet the growing demand for farmed salmonid products. Hemp processing by-products show promise as potential aquafeed ingredients due to their good profiles of essential amino acids (EAAs) and polyunsaturated fatty acids (PUFAs) beneficial for fish growth and health. We have extracted kilogram-scale quantities of protein isolates (PIs) and oils from the hemp by-products, hemp cake and hemp seed hulls and evaluated their nutritional composition. High protein digestibility of >88% was observed for hemp PIs when evaluated using a two-phase in vitro gastric/pancreatic protein digestibility assay (IVGPD). Findings from both in vitro digestion assays and an in vivo feeding study with rainbow trout indicate that hemp processing by-products have potential as a novel aquafeed ingredient feedstocks, benefiting sustainable aquaculture practices.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".