Mapping of Research Productivity on Nanotechnology in Canada: A Scientometric Profile
Bibliographic record
Abstract
A scientometric assessment of the scientific publications has been considered in this analysis by examining annual growth rate of publications, collaborative countries, and territories, preferred subject areas and research work, prolific organizations and institutions and top-ranked journals and highly productive papers etc. This paper focuses on the literature growth and development in Nanotechnology in Canada as reflected in the web of science data database. During the period between 1994 and 2014, a total 576 scientific research papers along with cited references are 34955 were published in the field in Canada. The average number of literature output were published per year was 33.88 and the greatest number of publications were published in 2013 and 2014 respectively a total number of authors 2213 were identified and the maximum number of authors i.e. 364 and the mean value of 4.77 were in the year 2014. Out of 15804 citations, the greatest number of 2791 citations in the year 2008 (52 papers, 23 h-index) and highest average citation per paper were 60.74 in the year 2007. From this study, researchers, scientists, subject specialists, students, administrators, policy makers, academicians, Library and Information Science professionals, and faculty members will be benefited due to the scientific and effective investigation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.100 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".