Metamaterials Research: A Scientometric Assessment of Global Publications Output during 2007-16
Why this work is in the frame
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Bibliographic record
Abstract
<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The paper examines 9858 global publications output on metamaterials research, as covered in Scopus database during 2007-16. The study reveals that metamaterials research registered 15.27% growth and averaged citation impact to 10.08 citations per paper. The global share of top 10 most productive countries in metamaterials research is 84.97 % and their individual global share ranged from 3.30% to 25.57%. China accounted for the largest global share (25.71%), followed by USA (23.96%), U.K. (6.06%), India (5.26%), etc. Five of top 10 countries scored relative citation index above the world average i.e. more than 1: Germany (2.06), USA (1.81), U.K. (1.49), Canada (1.03) and Spain (1.01). The international collaborative publications share of top 10 most productive countries varied from 6.14% to 59.80%. Physics and astronomy, among subjects, contributed the largest publication share (59.36%), followed by engineering (56.71%), materials science (33.30%), computer science (20.32%), mathematics (6.74%) and chemistry (4.46%). The top 20 most productive organisations and authors together contributed 24.69% and 13.17% global publications share respectively and 35.72% and 25.96% global citation share respectively. The top 20 journals accounted for 45.97% share of global output (5743 papers) reported in journals. Of the total global output on metamaterials research, 52 papers were found as highly cited papers averaging 535.64 citations per paper in 10 years. These 52 highly cited papers involved the participation of 310 authors and 142 organisations and were </span><span>published in 20 journals. </span></p></div></div></div>
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.146 | 0.149 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it