Nutritional considerations to inform nutritional safety assessments of foods derived from cellular agriculture: A scoping review protocol
Bibliographic record
Abstract
Cellular agriculture is the production of meat, milk, eggs, seafood, and other products and ingredients from cell cultures rather than from farmed animals. It is an emerging field in food innovation that has gained attention of the scientific community, food industry, and regulators around the world. This scoping review aims to summarize existing knowledge on cell-cultured food products, focusing on key aspects that impact their nutritional quality and identifying knowledge gaps in this area. Relevant publications will be identified from electronic databases and the grey literature. This review will include scientific publications (peer-reviewed journal articles; reports from regulatory, scientific, or non-governmental bodies) that report or discuss nutritional considerations concerning cell-cultured food products derived from plant or animal cells. Based on pre-determined inclusion and exclusion criteria, records from scientific databases will be screened and selected by two independent reviewers, while records from grey literature will be screened by one reviewer. Data from the included references will be extracted by one reviewer using pre-piloted extraction templates, and verified by a second reviewer. The findings will inform and strengthen Health Canada's nutritional assessments of novel foods derived from cellular agriculture. Identified research gaps will provide opportunities for future research in this area.
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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 | 0.107 | 0.116 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".