Navigating Türkiye's Energy Horizon: A Bibliometric Exploration of Academic Contributions to Energy, Fuels, and Hydrogen Subjects
Bibliographic record
Abstract
This paper aims to unveil the intellectual structure and knowledge flow within Türkiye's academic landscape, shedding light on influential research clusters and highlighting the interconnections between different research themes. The manuscript also synthesizes findings from a Web of Science database, elucidating the growth trajectories of Türkiye's contributions to the global discourse on energy, fuels, and hydrogen. Additionally, the role of interdisciplinary collaboration has been explored and the impact of Türkiye's research output on the international stage has been assessed. According to results, the oldest date goes back to 1972 for energy&fuels topic and 1989 for hydrogen topic. Whereas Ayhan Demirbas and Ibrahim Dincer are the most productive authors, Istanbul Technical University and Yildiz Technical University are the most productive institutions. Moreover, USA and Canada are the most efficient countries for colloborations. Last of all, while new trends in Energy&Fuels publications have been observed as machine learning, supercapacitor, nanoparticles, electric vehicle and graphene, new trends in hydrogen publications were observed as methanolysis, multigeneration, ammonia, thermodynamic analysis and graphene.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.062 | 0.101 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".