Scoping study of research trends on Nili Ravi buffalo applying scientometric analysis and network visualization
Bibliographic record
Abstract
The study elucidates scientometric analysis of published scientific communications on Nili Ravi buffalo in journal(s) for having an appraisal of status-quo of the research and scientific activities. Metadata of 383 articles retrieved from Scopus were analysed to identify the most productive author(s), institution(s) vis-a-vis countries and to ascertain their collaboration trends. Keyword based analysis was performed to provide an overview of the strength areas of research on Nili Ravi for better comprehension. The results revealed that the research efforts on Nili Ravi were discernible after the year 2005. All except 1.30% articles have been an outcome of the collaborative authorship. There were only few productive authors with ≥10 records, but others contributed on the subject occasionally. Nearly 90% of the articles have been contributed by Pakistan and its' authors have worked in close collaboration with scientists from United Kingdom, United States of America, China, Canada, and South Korea. They also have conjoint symbiosis on academic/research endavours on Nili Ravi with experts from Austria, Netherlands, India, Germany, Italy, and Australia. Twelve leading institutions contributed to ≥10 articles. Publication outcome of the Animal Sciences Institute, National Agricultural Research Centre, Islamabad, Pakistan; Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan; University of Gujrat, Gujrat, Pakistan and Semen Production Unit, Qadirabad, Sahiwal, Pakistan has higher Relative Citation Impact (RCI), making it obvious that their publication(s) have wider acceptance amongst scientific populace. Most productive vis-à-vis impactful journals publishing articles on Nili Ravi have also been identified.
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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.094 | 0.109 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".