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Record W4387606234 · doi:10.47552/ijam.v14i3.3629

Screening of Phytoresources from the Romanian Flora with medical applications against Covid - Review

2023· article· en· W4387606234 on OpenAlexfundno aff
Rodica D. Catană, Mirela Moldoveanu, Raluca A. Mihai, Anca Botezatu, Adrian Albulescu, Anush Kosakyan, Larisa I. Florescu

Bibliographic record

VenueInternational Journal of Ayurvedic Medicine · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and Biological Activities
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchAcademia Româna
KeywordsFlora (microbiology)Coronavirus disease 2019 (COVID-19)Context (archaeology)RomanianPandemicBiologyTraditional medicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Immunostimulant2019-20 coronavirus outbreakMedicineVirologyImmunologyDiseaseInfectious disease (medical specialty)PathologyImmune system

Abstract

fetched live from OpenAlex

Plants are an important means of combating numerous harmful influences on humans (microorganisms, viruses, fungi, etc.) and have always been used to treat various diseases. In the context of the Covid pandemic, interest in the use of plants has increased. Great importance has been given to screening plants' potential against Covid (antiviral, anti-inflammatory, immunostimulatory, and antioxidant). According to recent research, many of the plant species used against Covid are of Asian origin. In this review, we aim to discuss the plant species with these medicinal potentials with a focus on Romanian flora. We have listed a total of 50 Phyto-resources from Romanian flora with different potentials: 26 containing the confirmed anti-covid compounds, and 9 species having antiviral, anti-inflammatory, immunostimulant, and antioxidant potentials.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.283
Teacher spread0.245 · 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 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

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