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Record W4403119764 · doi:10.1002/9781394241576.ch13

Nutraceutical Potential of Herbal Beverages

2024· other· en· W4403119764 on OpenAlexaff
Anoma Chandrasekara, Sashya Diyapaththugama, Fereidoon Shahidi

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNutraceuticalFood scienceHerbal supplementTraditional medicineChemistryMedicineAlternative medicine

Abstract

fetched live from OpenAlex

A variety of herbal beverages are consumed worldwide. Globalization facilitates the expansion of herbal beverage market beyond cultural and geographical boundaries. The Covid-19 pandemic has led to a significant growth in demand for herbal beverages due to their functionality in many regions of the world. Recent advances of phytochemical research have revealed the groups of available bioactive compounds of herbal beverages. Different morphological parts of herbs and plants, such as leaves, stem, bark, roots, flowers, and fruits, belonging to wide array of maturity stages are usually used. Herbal beverages can be used in everyday life as a component of the balanced diet, thus to enhance the antioxidant status and attenuate inflammatory conditions improving health and wellness. The constituent plant bioactive agents found in herbal beverages include phenolic acids, flavonoids, terpenoids coumarins, polyacetylenes, carotenoids, saponins, and alkaloids. An abundance of scientific evidence demonstrate that these phytochemicals provide numerous bioactivities, namely, antioxidant, anti-inflammatory, antibacterial, antiviral, antiallergic, antithrombotic, and vasodilatory actions, in addition to antimutagenic, anticarcinogenic, and antiaging properties among others. Moreover, tendency of consumer choices for natural, minimally processed, ethically sourced, healthy food products remains a main driver for increasing the global use of herbal beverages.

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 categoriesInsufficient 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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.012
GPT teacher head0.293
Teacher spread0.281 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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