Research Productivity of Life Sciences from 2014-2016: A Scientometric Study through Web of Science Database
Bibliographic record
Abstract
This paper attempts to analyse quantitatively the growth and development of ‘Life Sciences’ research publication output as reflected in the Web of Science (WOS) database from 2014 to 2016. A total of 9941 papers published by the researchers in the Research areas Life Sciences. The Year wise analysis shows that Life Sciences reflected with 3313 papers per year. In the year 2016 topped with 3824 publications and the lowest were in the year 2014 with 2711 publications. The trend shows that Life Sciences Research productivity has gradually increased. Science Technology and other topics topped with 672 documents (6.72%) published, most of them are Journal article as document type with 7395 (74.39%) and the most preferred source is Co Charane Database of Systematic Reviews 215 (2.16%) to publish. The country-wise distribution shows that the USA ranked topped with 3151 (31.70%) publications. India published 242 (2.43%publications with 13th position). Among the authors, a total of 31 records published without the author name mentioned with Anonymous. Guruwwamy K S ranked first and published 25 articles, Davidson B R with 19 and Liu Y with 17. So many organizations/Institutions have published the articles in Life Sciences. Harvard University has published 131 (1.32%), followed by UCL 101 (1.02%), University Toronto 97 (0.98%) and so on.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.061 | 0.106 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".