Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis
Bibliographic record
Abstract
This bibliometric analysis explores machine learning applications in biofuels and biodiesel research using Elsevier's Scopus database from 2013 to 2023. The research employs co-authorship, co-occurrence, citation, and co-citation analyses with fractional counting. Results indicate a significant rise in publications. Prominent funding agencies along this field include the National Natural Science Foundation of China, Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico and the U.S. Department of Energy. Co-authorship analysis reveals contributions from 268 authors across 951 organizations in 71 countries, with strong collaboration in Asia. Citation analysis shows that 95% of articles have received at least one citation, with China and the United States leading in citation counts. This study highlights the interdisciplinary and collaborative nature of machine learning research in biofuels and biodiesel, driven by substantial contributions from key funding bodies and researchers worldwide.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.130 | 0.241 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".