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Record W4322760585 · doi:10.3390/plants12051098

Studies on Cistanches Herba: A Bibliometric Analysis

2023· article· en· W4322760585 on OpenAlex
Longjiang Wu, Tian Xiang, Chen Chen, Murtala Bindawa Isah, Xiaoying Zhang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlants · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and Biological Activities
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGeographyChinaBiologyTraditional medicineMedicine

Abstract

fetched live from OpenAlex

As a famous tonic herb, Cistanches Herba is known for its broad medicinal functions, especially its hormone balancing, anti-aging, anti-dementia, anti-tumor, anti-oxidative, neuroprotective, and hepatoprotective effects. This study aims to provide a comprehensive bibliometric analysis of studies on Cistanche and to identify research hotspots and frontier topics on the genus. Based on the metrological analysis software CiteSpace, 443 Cistanche related papers were quantitatively reviewed. The results indicate that 330 institutions from 46 countries have publications in this field. China was the leading country in terms of research importance and number of publication (335 articles). In the past decades, studies on Cistanche have mainly focused on its rich active substances and pharmacological effects. Although the research trend shows that Cistanche has grown from an endangered species to an important industrial plant, its breeding and cultivation continue to be important areas for research. In the future, the application of Cistanche species as functional foods may be a new research trend. In addition, active collaborations among researchers, institutions, and countries are expected.

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.

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 categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.057
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.298
Teacher spread0.183 · 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