Tracking Research of Indian Council of Agricultural Research: Insights From Scientometric Analysis
Bibliographic record
Abstract
This study examines the research output of Indian Council of Agricultural Research (ICAR) researchers from 2010 to 2023 using scientometric tools and the Web of Science database. Initially identifying 2956 articles, subsequent application of exclusion criteria yielded 2950 relevant documents, encompassing journal articles, reviews, conference papers, and other scholarly contributions. The analysis reveals a robust scientific output characterized by recent publication dates, underscoring the timeliness of ICAR’s research. Key journals such as “PLOS ONE,” “Frontiers in Plant Science,” and “Scientific Reports” emerge as significant platforms for disseminating ICAR’s findings. The Indian Agricultural Research Institute (IARI) stands out for its substantial research output and citation impact. Collaboration is a prominent feature, with many documents being co-authored, reflecting the interdisciplinary nature of ICAR’s research and facilitating knowledge exchange among researchers. The study employs Biblioshiny (Bibiliometrix) and VOSviewer software for bibliometric analysis, providing insights into growth trends, collaborative patterns, authorship trends, and institutional collaborations at both national and international levels.
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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 | 0.040 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.109 | 0.263 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".