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Record W2341851686 · doi:10.1039/9781849734981-00065

Legislation for Food Additives Outside Europe

2013· book-chapter· en· W2341851686 on OpenAlexaboutno aff
Vanessa Richardson, Ella Freeman, Laura Fitzpatrick, Linda Amirat, Mariko Kubo, Meng Li

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationBusinessEuropean unionFood additiveInternational tradeConsumption (sociology)CommissionWork (physics)Food safetyAgricultureAgricultural economicsGeographyFood sciencePolitical scienceEngineeringEconomicsLaw

Abstract

fetched live from OpenAlex

Controls on the use of food additives can vary significantly from country to country and area to area, having the potential to represent a serious issue to manufacturers of food products and importers who need to purchase ingredients from different parts of the world and exporters intending to sell their products in more than one market. Different factors must be taken into consideration in the development of food legislation related to additives, including technological need and safety. The Codex Alimentarius Commission (CAC) and its Committees, through the work with the Joint Food and Agriculture Organization (FAO)/World Health Organization (WHO) Expert Committee on Food Additives (JECFA) are responsible, at international level, for the evaluation of food additives to ensure they are safe for consumption taking into consideration patterns of consumption of the foods in which additives are used. Therefore, the work of the Codex Alimentarius has a severe impact on the development of food legislation worldwide, together with the work of regional bodies, such as the European Union (EU) bodies. The use of food additives also follows trends led by consumer perception. The aim of this chapter is to assess food additive controls and key aspects of international food law or guidance laid down in several countries/regions and international bodies, namely the Codex Alimentarius, the USA, Canada, Japan and certain Far East countries, the Southern Common Market (MERCOSUR), the Middle East and Australia/New Zealand, with the aim of highlighting some of the main differences.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0390.028

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 head0.048
GPT teacher head0.272
Teacher spread0.224 · 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
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

Citations2
Published2013
Admission routes1
Has abstractyes

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Same topicConsumer Attitudes and Food LabelingFrench-language works237,207