Bibliometric Analysis of Human Metapneumovirus Studies
Bibliographic record
Abstract
Objective: This study aimed to estimate research productivity and general characteristics of research in different regions of the world in the field of human metapneumovirus (hMPV). Materials and Methods: We used the Web of Science (WoS) electronic database to retrieve articles on "human metapneumovirus," published up to March 31, 2023. Parameters such as research productivity, the total number of publications, average citation counts of all articles, research productivity by top publishing countries and affiliated organizations, links between countries/organizations, and top keywords were assessed. Results: We found 826 articles in the WoS database. The first article on hMPV was published in 2002, and 2008 was the year that the most articles on hMPV were published, with 70 articles. The articles were cited 26,797 times (14,498 times without self-citations) and 32.25 times on average per article. Hirsch (H)-index was 80. The year 2008 also had the highest number of citations, with more than 1800. The United States was the most productive country, producing 295 documents (35.714%), one-third of all publications in this field worldwide. Canada (6.5%) and Japan (6.5%) followed the United States regarding total hMPV publications worldwide. The leading funding agencies for hMPV research were from the United States, Europe, China, and Japan. Conclusion: Our findings imply that developed countries should assist developing countries in enhancing their research infrastructures.
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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 | 0.015 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.169 | 0.203 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".