MétaCan
Menu
Back to cohort
Record W4405978870 · doi:10.53555/sfs.v10i1.3266

Morphology, Phytochemistry, and Pharmacology of constituent plants of Trikatu in Ayurveda- A review study.

2023· review· en· W4405978870 on OpenAlexvenueno aff
Anil Kumar K. K

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typereview
Languageen
FieldMedicine
TopicPhytochemicals and Medicinal Plants
Canadian institutionsnot available
Fundersnot available
KeywordsPhytochemistryTraditional medicineClinical pharmacologyMedicinePharmacology

Abstract

fetched live from OpenAlex

Trikatu, an ancient herbal formulation in Ayurvedic medicine, is composed of three pungent spices: black pepper (Piper nigrum), long pepper (Piper longum), and ginger (Zingiber officinale).These ingredients are revered for their potent digestive, respiratory, and metabolic benefits.Trikatu is traditionally used to balance the Kapha and Vata doshas, enhance Agni (digestive fire), and promote the bioavailability of nutrients and medications through its ability to improve digestion and absorption.This synergistic blend acts as a natural stimulant, carminative, and expectorant, helping alleviate respiratory issues such as cough, cold, and asthma.Its thermogenic properties aid in fat metabolism and support weight management, making it a popular remedy for managing obesity and hyperlipidaemia.Scientific research corroborates many of Trikatu's traditional uses, indicating that its bioactive compounds, particularly piperine and gingerols, exhibit anti-inflammatory, antioxidant, and immunomodulatory effects.Additionally, Trikatu enhances the absorption of various drugs and nutrients, a property known as bio enhancement, which makes it a valuable adjunct in therapeutic formulations.However, further clinical studies are needed to explore its full therapeutic potential and establish standardized dosages for diverse health conditions.Overall, Trikatu represents a powerful, natural remedy that bridges the gap between traditional Ayurvedic wisdom and modern science.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.481
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.391
GPT teacher head0.433
Teacher spread0.043 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicPhytochemicals and Medicinal PlantsFrench-language works237,207