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Record W4404494128 · doi:10.1002/9781394270576.ch19

Regulatory Aspects of Antioxidants

2024· other· en· W4404494128 on OpenAlexaboutno aff
Kiran Kumar Bellapu, Tejaswini Mergu, Nikhil Vinod Shirsath, Pooja Anil Shende, Pavankumar Yashvantrao Vasu, Deepak Kumar Jindal, Parul Grover, Saurabh Srivastava, Rakesh K. Sindhu, Sandeep Kumar

Bibliographic record

VenueAntioxidants · 2024
Typeother
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryComputational biologyBiology

Abstract

fetched live from OpenAlex

To stop oxidative damage in the human body, antioxidants are frequently utilized in both food and pharmaceutical products. Oxidative damage is one of the root causes of many diseases, such as cardiovascular, Alzheimer's, cancer, etc. Antioxidants such as ascorbic acid, butylated hydroxytoluene, propyl gallate, butylated hydroxy anisole, and sodium metabisulphite are common excipients that are used in the finished product to reduce the oxidation of active substances. They could come from either natural or synthetic sources. Protecting food goods from oxidation-related deterioration, such as rancid fat, color changes, and nutritional loss, extends their shelf life. The approval and marketing of antioxidants based on the benefit-to-risk ratio are heavily influenced by regulatory bodies such as the European Economic Commission (EEC) in Europe, the Food and Drug Administration (FDA) in the United States, and the Food and Drug Regulations in Canada. The safety, quality, and efficacy information is essential to submit in a common technical document at the time of excipients approval. The amount of antioxidants in the food products is indicated by a nutrient content claims on the label. Moreover, inadequate doses and durations of therapy in clinical trials could explain the lack of positive outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.227
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.003

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 head0.010
GPT teacher head0.243
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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