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Record W4400148174 · doi:10.1016/j.cdnut.2024.103644

Identifying Nutritional Considerations to Inform Safety Assessments of Foods Derived From Cellular Agriculture: A Scoping Review Protocol

2024· review· en· W4400148174 on OpenAlexaffabout
Karima Benkhedda, Matthew D. Parrott, Atiq Ur Rehman, Subhadeep Chakrabarti, Stephanie Nishi, Vincent CH Wong, Laura Kenney

Bibliographic record

VenueCurrent Developments in Nutrition · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsHealth Canada
Fundersnot available
KeywordsProtocol (science)AgricultureRisk analysis (engineering)Environmental planningBusinessEnvironmental resource managementEnvironmental healthEnvironmental scienceMedicineGeography

Abstract

fetched live from OpenAlex

Objectives: Cellular agriculture is an emerging field in food innovation that has gained the attention of the scientific community, food industry, and regulators around the world. This scoping review aims to systematically identify and summarize existing knowledge on nutritional aspects of foods derived from cellular agriculture to characterize related hazards to consumer health, and to highlight knowledge gaps 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.145
metaresearch head score (Gemma)0.130
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.145
Threshold uncertainty score0.768

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1450.130
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0100.015
Bibliometrics0.0200.014
Science and technology studies0.0060.005
Scholarly communication0.0090.009
Open science0.0060.009
Research integrity0.0110.007
Insufficient payload (model declined to judge)0.0510.017

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 head0.132
GPT teacher head0.429
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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
Published2024
Admission routes2
Has abstractyes

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