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Record W4404078311 · doi:10.53555/sfs.v10i1.3126

“An Analysis Of Manasa And Deha Prakriti And Their Significance In Vyadhi Prevention”

2023· article· en· W4404078311 on OpenAlexvenueno aff
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Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesia

Abstract

fetched live from OpenAlex

Prakriti is one of the unique concepts of Ayurveda. It aids in both disease management and diagnosis. Every Acharya explains and mentions the concept of Prakruti in detail. The acharya explained that the vyadhi nimitta prakriti's fundamental idea also aids in maintaining the equilibrium of healthy individuals' health. The Prakriti dictates the qualities and functions of everybody, according to Ayurvedic Principles. A significant part is played by sharir and manas prakriti in hetu, linga, and aushadh askandha. Vyadhi is the opposite state of health; without an understanding of a person's Deha Prakriti, it is nearly impossible to diagnose and effectively treat an individual using the core principles of Ayurveda for the promotion of health, avoidance of disease, and effective management. Additionally, Agni (digestive fire), Koshtha (food intake & digestive capacity), and an individual's Agni are all influenced by Prakriti. Diet, dietetic guidelines, and lifestyle choices are all crafted in accordance with Prakriti. Prakriti is so crucial for managing health issues and preventing illness. Prakriti, which depicts a person's whole physiological and psychological makeup, has an impact on day-to-day existence. Understanding this will make it easier to select a lifestyle that fits one's Prakriti in terms of eating habits, exercise routines, jobs, and other factors. The purpose of this paper is to investigate the idea of Prakriti within the framework of Vyadhi and to determine how Prakriti and Vyadhi are related.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.215
GPT teacher head0.294
Teacher spread0.079 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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