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Nutraceuticals: New Era of Medicine and Health

2023· article· en· W4386309124 on OpenAlexaboutno aff
Pranav Jadhav -, Laxmi Koli -, Siddhi Kolwadkar -

Bibliographic record

VenueInternational Journal For Multidisciplinary Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsNutraceuticalDiseaseMedicineAlternative medicineObesityPillTraditional medicinePharmacology

Abstract

fetched live from OpenAlex

The concept of Nutraceuticals was first introduced in the survey from the UK, Germany and France, where diet is rated more highly by consumers than exercise or hereditary factors for achieving good health. The word nutritional is a blend of the words nutrition and pharmaceuticals. Nutraceuticals, in broad terms, are foods or parts of foods that play a significant role in modifying and maintaining normal physiological function that maintains healthy human being. “Let food be your medicine’’ is a common misquotation attributed to Hippocrates, who is the father of western medicine, according to the health Canada. Nutraceuticals are foods or products prepared from food but sold in the forms of pills, powder, etc. (portion) or in other medicinal forms not usually associated with food. The benefits of Nutraceuticals are limitless as well as effective in everyday life, from physiological heath Nutraceuticals have the potential to treat a wide array of illness and ailments. Recent studies have shown promising results for the effectiveness of herbal Nutraceuticals on disorder related to oxidative stress, including Alzheimer’s, cardiovascular disorders, cancers, diabetes, inflammatory disease ,Parkinson’s disease ,and obesity ,in whole , “ nutritional ”has provide a route to a new era of medicine and health ,in which the food industry has become a research oriented sector.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.014
Scholarly communication0.0060.010
Open science0.0010.004
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0100.003

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 head0.233
GPT teacher head0.539
Teacher spread0.306 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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Same venueInternational Journal For Multidisciplinary ResearchSame topicNutrition, Genetics, and DiseaseFrench-language works237,207