ANÁLISE BIBLIOMÉTRICA DA PRODUÇÃO DE BIOCHAR COM PROCESSO DE PIRÓLISE PARA O TRATAMENTO DE PASSIVOS DE ATERROS INDUSTRIAIS: ÊNFASE NA AVALIAÇÃO DO CICLO DE VIDA
Bibliographic record
Abstract
The production of biochar, or coal, by the pyrolysis process has been studied in the comparison of climatic environmental impacts with life cycle analysis (LCA) by several authors. In this bibliometric study, the VOSviewer 1.6.12 tool and the Web of Science database were applied to account for the number of publications considering the period and countries, as well, the authors with the most published articles on the chosen topic and other related terms. . Various combinations of research terms were investigated, with Life Cycle Assessment and Pyrolysis and Biochar being the broadest reference of publications: 121 articles. Life Cycle Assessment and Pyrolysis and Catalytic offered 14 research articles, with limitations as to the fact that it does not work with the so-called real samples. United States, China and Canada appear as the countries with the largest number of publications, with the six most prominent authors published between 4-6 articles. However, the investigated correlation terms show on average growing interest in studies in recent years, especially between 2015 and 2019. In addition to Life Cycle Assessment, the Life Cycle Perspective and Life Cycle GHG Emission studies are highlighted in the research.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.154 | 0.177 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".