Glycyrrhiza glabra (Medicinal Plant) Research: A Scientometric Assessment of Global Publications Output during 1997-2016
Bibliographic record
Abstract
The present study examined 3428 global publications in Glycyrrhiza glabra, as covered in multidisciplinary Scopus bibliographical database during 1997-2016, with a view to understand their growth rate, global share, citation impact, international collaborative papers share, distribution of publications by broad subjects, productivity and citation profile of top organizations and authors, preferred media of communication and bibliographic characteristics of high cited papers. The global publications registered an annual average growth rate of 10.87% and its citation impact averaged to 19.09 citations per paper. Among the top 12 most productive countries, the global share ranged from 1.87% to 19.81%, with China contributing the largest share of 19.81%, followed by India (13.71%), USA (11.84%), etc. More than 79.0% of the cumulative global publication share comes from top 12 countries during 1997-2016, showing decrease from 100.0% to 77.80% from 1997-2006 to 2007-16. Seven countries registered relative citation index above the world average of 1.10: U.K. (2.39), USA (1.87), Canada (1.71), Italy (1.51), Japan (1.49), Turkey (1.24) and Taiwan (1.18) during 1997-2016. Medicine, among seven broad subjects, contributed the largest publications share of 44.41%, followed by pharmacology, toxicology and pharmaceutics (35.04%), biochemistry, genetics and molecular biology (26.84%), agricultural and biological sciences (16.89%), chemistry (14.59%), etc. during 1997-16. Among various organizations and authors contributing to global Glycyrrhiza glabra research, the 20 most productive global organizations and authors together contributed 15.08% and 9.16% global publication share respectively and 14.57% and 16.62% global citation share respectively during 1997-16. Amongst 3322 journal papers (in 1153 journals) in global Glycyrrhiza glabra research, the top 20 most productive journals contributed 16.80% share of total journal publication output during 1997-2016. One hundred thirteen (113) publications were found to be high cited, as they registered citations from 100 to 852 during 1997-2016 and they together received 22234 citations, which averaged to 196.76 citations per paper.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.088 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".