PRIMARY JOURNALS AND THEIR COUNTRIES IN THE FIELD OF DENGUE LITERATURE: AN ANALYSIS
Bibliographic record
Abstract
Presents a bibliometric analysis of the literature in the field of Dengue as indexed the MEDLINE data which covered in the Pubmed for the period 2008 to 2017. It is noticed that total of 11826 records on literature of Dengue are covered for a period of ten years from 2008 to 2017. It is also noticed that the maximum number of records (1810) was published during year 2016, followed by 1540 in 2017 and 1520 in 2015. It was found that Journal Article (41.4%), Research Support, Non-U.S. Gov’t (33.81%), Review (10.69%), Letter (3.61%), and Research Support, U.S. Gov't Non-P.H.S. (2.86%). 37 primary journals grouped in zone 1 published 1675 articles accounting for one third of the total output. Similarly the second zone comprises of 143 journals and 904 journals grouped in third zone. Of the 37 titles in zone-1, 12 are associated with United States and followed by England (9), Netherlands (5), India (4), Brazil (2), China (1), Germany (1), Japan (1), Sweden (1) and Thailand (1). In zone-1 & 2 ; out of 180 journals, 51 frequently cited journals are United States, this is followed by the countries i.e. England (33), India (19), Netherlands (11), Brazil (9), Switzerland (9), France (6), Japan (4), China (3), Egypt (3), Germany (3), Pakistan (3), Singapore (3), Colombia (2), Italy (2), Malaysia (2), Thailand (2), Argentina (1), Australia(1), Austria (1), Canada (1), Chile (1), Cuba (1), Indonesia (1), Iran (1), Jamaica (1), Mexico (1), Peru (1), Philippines (1), Poland (1), Sri Lanka (1) and Sweden (1).
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.088 | 0.141 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".