Trend in Publications Related to Biomethane Using a Bibliometric Approach
Bibliographic record
Abstract
The emission of greenhouse gases emitted into the atmosphere by the burning of fossil fuels has allowed the development of biofuels such as biomethane.The aim of this research was to explore the characteristics of biomethane literature from 1978 to 2020 based on the database of Scopus and its implications using indicators bibliometrics.The information in the database was analyzed through the Gompertz model to determine the specific growth rate over the years.Also, maps were elaborated with the VOSviewer software to show in a visual way the collaboration between authors and keywords related to the topic of study.Documents were examined in a variety of aspects of the publication characteristics such as document type, language, authorship, countries, institutions, journals, high-cited papers.Results showed that the evolution of publications grew exponentially from 2006 to 2020, the specific speed of growth determined with the Gompertz model was 0.4 0.095 years -1 (𝑅 2 >0.99).52% of publications are concentrated in five countries (Italy, India, China, the United States and Spain).Bioresource Technology is the journal with the highest number of publications and citations, the authors publish in quartile 1 and 2 journals.The biomethane topic has a growing number of publications and citations due to the collaboration between researchers from different countries.The present work can be contrasted with the analysis of bibliographic indicators with other databases to determine the level of collaboration with other journals with different index.
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How this classification was reachedexpand
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 | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.059 | 0.093 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".