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Record W6885384752 · doi:10.13590/j.cjfh.2020.04.019

Comparative analysis of domestic and foreign beverage standards and regulation indicators

2020· article· en· W6885384752 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationFood safetyBeverage industryQuality (philosophy)Safety standardsChinaOrder (exchange)Commission

Abstract

fetched live from OpenAlex

Objective In order to promote and guide the development of beverage industrialization and standardization in China, this paper studies the management of beverage regulations and standards by international organizations and other countries, to summarize the problems and differences in domestic and foreign beverage standards, to put forward suggestions for the improvement of China's beverage quality and safety standards, and to provide references for the revision of China's beverage safety standards. Methods Comparing the standards and regulations of China, Codex Alimentarius Commission (CAC), the European Union, the United States, Australia/New Zealand, Canada and Korea, analyzing the categories, heavy metals, mycotoxins, microorganisms and other indicators, studying the existing problems in the current beverage standards in China, and putting forward some useful suggestion. Results The management of beverage standards in other countries is different from that in China. The formulation of food safety standards of beverage in China is relatively reasonable and basically identical to international standards. We should pay more attention to the colony count indicators, the ready-to-consume drinks, the improvement of standards in beverage categories, and the effective coordination between quality and safety standards. Conclusion China's beverage standard management system conforms to China's domestic situation. The beverage food safety standards is relatively reasonable. China should take good measures to the food safety standards and quality standards revision on beverages, and promote the healthy and harmonious development of the beverage industry.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.204
GPT teacher head0.500
Teacher spread0.296 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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