Tracking Research of Indian Council of Agricultural Research: Insights From Scientometric Analysis
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.022 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it