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Record W2804288165

PRIMARY JOURNALS AND THEIR COUNTRIES IN THE FIELD OF DENGUE LITERATURE: AN ANALYSIS

2018· article· en· W2804288165 on OpenAlexaboutno aff
Kotti Thavamani

Bibliographic record

VenueLincoln (University of Nebraska) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsDengue feverField (mathematics)Political scienceVirologyMedicineMathematics
DOInot available

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.264
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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