Scientometric portrait of Professor CNR Rao using bibliometrix R package
Bibliographic record
Abstract
The study reveals a long research experiences of Professor CNR Rao. In his 85 years of life and research experiences, he published 1648 articles, which are indexed in Scopus database during 1956-2019 with average citation per paper is 52.01. But, he might have published a few hundred more papers, which are not indexed in Scopus are not considered in this study for analysis. A collaboration co-authorship and co-occurrences networks of the author were built using Bibliometrix R Package. Author co-citation network, co-authorship and authors’ coupling were built using R Package. The major findings of the study show that the highest collaboration happened with the USA and UK, France, Canada and Japan. The result shows that he collaborated with 1156 unique authors from various countries with co-authors per document is 3.81 and collaborative index is 0.73. Rao is having 1,19,169 citations from Google Scholar with h-index (156) and i10-index (1325). The result shows that CNR Rao preferred to communicate most of his papers to the Chemical Physics Letters, Journals of Solid State Chemistry, Solid State Communications, Journal of Materials Chemistry etc.
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.091 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.022 |
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".