Global research trends in wood pellets, a renewable energy: a bibliometric analysis
Bibliographic record
Abstract
Due to global warming and climate change, more importance has started to be given to renewable energy sources instead of fossil fuels in recent years. Wood pellets are also one of the renewable energy sources and a growing number of studies have been conducted regarding wood pellets. Therefore, developments and trends in the field of wood pellets can be determined through bibliometric analysis of publications. This study aimed to explore the current status and current hot topics of research on wood pellets between 1980-2023 using performance analysis and science mapping. For this purpose, we carried out a bibliometric analysis of 758 publications in the Web of Science database scanned with the keywords "wood" and pellets". The numbers of publications and cited citations on wood pellets have grown steadily over the years, with 67% of publications produced and 89% of citations cited in the last decade. 2294 Authors from 884 organizations and 6 continents contributed publications in the field of wood pellets. University of British Columbia (Canada) and United States Department of Energy (USA) were the major institutions with the largest publications and the most cited. Sokhansanj Shahab from the University of British Columbia was the most active and most cited author. The published literatures have focused on three topics: biomass, bioenergy and combustion. As a result, this study will provide a general perspective for future research in the field of wood pellets.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.104 | 0.207 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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, 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".