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Record W4319339626 · doi:10.2478/aoas-2022-0092

Cell-Based Meat Labeling – Current Worldwide Legislation Status – A Review

2023· review· en· W4319339626 on OpenAlexaboutno aff
Tomáš Vlčko, Krzysztof Bokwa, Iwo Jarosz, Andrzej Szymkowiak, Jozef Golian, Marcin Adam Antoniak, Piotr Kulawik

Bibliographic record

VenueAnnals of Animal Science · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsLegislationEuropean unionBusinessInternational tradeInvestment (military)Economic policyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract A growing interest has been noted among both industry operatives and consumers in cell-based meat (CBM), as visible in the increasing investment into this technology by major food industry corporations. However, in almost all countries worldwide, there is a lack of clear legislation with regard to the labeling of such products. The aim of the article is to collect and review current legal regulations concerning the international approval and labeling of these types of products. In the manuscript, we review and analyze the legal situation of CBM and its labeling in countries from 4 different continents (EU members, the UK, the USA, Canada, Australia and New Zealand, Japan, Singapore and Israel). Aside from Singapore, no other country has approved CBM for placement on the market. The US has reached an agreement and established regulatory frameworks on CBM matters, where both the USDA and the FDA will be the control institutions. Within the European Union, CBM products will be evaluated under the Novel Food Regulation. The most anticipated process in other countries is the evaluation of CBM under the legislation on novel foods and subsequent amendments. Since local laws are still being developed, special care should be taken by the policymakers to avoid implementing local laws which could cause a negative approach to the technology by the consumers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.116
GPT teacher head0.397
Teacher spread0.281 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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