Supply Chain Management of Biomass for Energy Generation: A Critical Analysis of Main Trends
Bibliographic record
Abstract
The increasing need of decarbonising energy intensive processes has risen the demand for biomass. Biomass production, distribution and use for energy generation involve several supply chain systems of which understanding requires a comprehensive analysis of the biomass supply chain management. The present article maps the volume and diversity of research carried out in the production and management of biomass supply chains for energy generation. It critically evaluates how well studies have captured multidimensional issues pertaining the supply chain management of biomass used for energy production and identifies future research trends in this field. The VOSviewer (Center for Science and Technology Studies, Leiden University, Leiden, The Netherlands) and SciMat (University of Granada, Spain) tools are employed for the construction of scientific maps that demonstrate the evolution of research in the biomass supply chain management area for energy production. The results revealed that research on the biomass supply chain for power generation is booming, especially in the United States, England and Italy. However, in developing nations such as Brazil, India and China, studies are still at an infant stage. There is a rising concern about the emerging new trends related to biomass supply chain management for energy generation, especially if clean energy aims to be a prominent place in the global energy matrix.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.001 |
| 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".