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Record W4414590081 · doi:10.1017/err.2025.10053

Could Pharmacopoeial Reference Standards Serve as a Platform to Enhance the Quality and Safety Control Systems of European Food Supplement Businesses?

2025· article· en· W4414590081 on OpenAlexaboutno aff
Roman Warda, Kai Purnhagen, Milica Molitorisová

Bibliographic record

VenueEuropean Journal of Risk Regulation · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural safety and regulations
Canadian institutionsnot available
FundersUniversity of Cambridge
KeywordsQuality (philosophy)LegislationFood safetyControl (management)PharmacopoeiaCompromiseCompliance (psychology)Good manufacturing practice

Abstract

fetched live from OpenAlex

Abstract Previous research has highlighted several quality-related concerns regarding food supplements available on the market, which compromise their safe consumption. This study evaluates whether the adoption of the European Pharmacopoeia (Ph. Eur.) as a framework for improving supplement quality could enhance quality and safety control practices. The findings are derived from a comparative legal analysis of the Canadian and U.S. legal systems. The results suggest that its application in the Canadian market may serve as an illustration of the Brussels effect in practice. Simultaneously, the European Food Safety Authority (EFSA) already encourages EU Food Business Operators (FBOs) to utilise the Ph. Eur. when assessing food supplement ingredients. Nevertheless, careful consideration is necessary regarding the extent of regulatory compliance by FBOs to mitigate potential conflicts with existing EU legislation and to prevent delays in innovative developments within the supplement market.

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)0.080
metaresearch head score (Gemma)0.079
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: Empirical · Consensus signal: none
Teacher disagreement score0.129
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0030.006
Scholarly communication0.0090.004
Open science0.0030.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.296
Teacher spread0.271 · 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
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

Citations1
Published2025
Admission routes1
Has abstractyes

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