Bibliometric Assessment of International Developments in Paper Sludge Research Using Scopus Database
Bibliographic record
Abstract
Abstract Energy viability and waste disposal have been the two significant global complication. The consumption of paper and, subsequently, recycling are increasingly growing, contributing to vast quantities of paper sludge. Therefore, in waste disposal and environmental remediation, coping with massive volumes of paper sludge has received tremendous attention worldwide. Our purpose was to assess leading study advancements globally of paper sludge based on articles published, authors intra/inter-collaborations and accumulations of keywords. Throughout entire 1967–2019 duration, 2096 publications in paper sludge topic were mined using Scopus database. The findings revealed that the number of publications was less than 30 between 1967 and 1995, less than 60 between 1996 and 2005, less than 90 between 2006 and 2010 and more than 90 between 2011 and the recent year. Consequently, the yearly publishing is forecast to keep to expand. In a total of 125 journals, a total of 217 Canadian scholars from 155 universities lead to 263 papers, comprising 10.8 % total publications, where 261 (99.2 %) of 263 total English-language publications dominate the other countries/territories, while 0.8 % in French language. Also, from each of the 15 top countries, among the most productive universities, Université Laval was ranked 251 st in World University Rankings 2021. In review, the following present developments in paper sludge comprise of: (i) cement, cellulose, bioethanol/biogas and concrete; (ii) phytoremediation and vermicompost and (iii) modelling (e.g., response surface methodology).
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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.206 | 0.270 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".