Mapping Research Trends in Publications Related to Bio-Jet Fuel: A Scientometric Review
Bibliographic record
Abstract
It is a fact that society has increased the need for mobility throughout the world. In that regard, it has become aware of the problems associated with the use of fossil fuels such as jet-fuel. As alter-natives, the use of bio-jet fuel has been proposed, which is a biofuel that researchers have evaluated and developed as an environmentally friendly alternative. The development of research on the topic of biofuels has generated a growing number of alternatives in the methods, technologies and raw materials for the production of bio-jet fuel. In this work, a bibliometric study has been developed to analyze the evolution of publications, the contribution of authors, countries, in terms of citation productivity on the topic of bio-jet fuel. Scientific publications were searched in the Scopus database for the period 2001 to 2021. The results showed that the publications have grown exponentially in the last 10 years. The most influential institution and country are from China. “Renewable and Sustainable Energy Reviews” is the most cited journal in the field of bio-jet fuel. The growth rate of publications was estimated using the Gompertz model, the rate was 0.2232 y-1. Most of the documents were published in journals Q1.
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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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.143 | 0.254 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".