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Record W4414826551 · doi:10.1016/j.mex.2025.103658

Nutritional considerations to inform nutritional safety assessments of foods derived from cellular agriculture: A scoping review protocol

2025· review· en· W4414826551 on OpenAlexaffabout
Karima Benkhedda, Matthew D. Parrott, Stephanie Nishi, Vincent Wong, Laura Kenney, Chao-Wu Xiao, Atiq Ur Rehman, Subhadeep Chakrabarti

Bibliographic record

VenueMethodsX · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsToronto Metropolitan UniversityHealth Canada
Fundersnot available
KeywordsGrey literatureProtocol (science)AgricultureQuality (philosophy)Scientific literatureData extractionFood safetyNovel foodInclusion and exclusion criteria

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.790
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.057
GPT teacher head0.422
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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