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Record W2946083913 · doi:10.5539/gjhs.v11n6p132

Comparative Study of Functional Food Regulations in Japan and Globally

2019· article· en· W2946083913 on OpenAlexvenueno aff
Mohamed Farid, Kota Kodama, Teruyo Arato, Takashi Okazaki, Tetsuaki Oda, Hideko Ikeda, Shintaro Sengoku

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsCertificationLegislatureChinaBusinessEuropean unionGovernment (linguistics)MarketingInternational tradePublic economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Since its inception in Japan, functional food has continued to deliver a true added value to a wide spectrum of customers, especially in aging subpopulations. Japanese companies have strong "R&D" capabilities and strong know-how in the field of functional foods. They have the opportunity to grow overseas by promoting and marketing their products. The main challenge is to understand the foreign markets and their regulations to be able to promote Japanese products overseas. To achieve this goal, the study reports a scientific review of the relevant literature and official legislative reports published by the authorized entities in several countries to create a comparison between the rules and regulations in different countries such as China, the European Union, South Korea, Singapore, Taiwan, and the United States. The study results provide suggestions for entry strategies to recommended foreign markets based on regulatory situations. The study also provides a comparison for the different functional food regulations in Japan (FOSHU, FNFC, FFC), along with an introduction for the new local government certification system.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.095

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.307
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2019
Admission routes1
Has abstractyes

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