Emerging research trends In MERS: A Scientometric study of Top 5 Countries
Bibliographic record
Abstract
Middle East respiratory syndrome (MERS), also known as camel flu, is a viral respiratory infection caused by the MERS-corona virus (MERS-CoV). Symptoms may range from mild to severe. They include fever, cough, diarrhea, and shortness of breath. Disease is typically more severe in those with other health problems. The Previous literature on the subject is very important for that; and the study of this literature is important on the point of view of the researchers. Mine paper focused on top 05 countries literatures on MERS. The collected data of top 05 countries will be analysed by different scientometrics techniques and parameters. The data was collected on the Pubmed Database in the period of 1991 to 2015 about the number of author’s literature of countries on MERS. The total number of authors was 29136 of that literature and top 05 countries were produce lot of literature on MERS i.e. United states, England, Netherlands, Canada and Germany respectively. The total no. of 4374 documents literatures was produced during the study period.
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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.105 | 0.152 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".