Biomass—a Renewable Resource for Carbon Materials (2nd Edition)
Bibliographic record
Abstract
The effort of the Editors has received global attention. Indebtedness is due to Saha and Asif and coworkers from Canada, Joseph and Mahapatra and coworkers from Poland and India, Ramirez-Montoya and coworkers from Spain and Mexico, Mota-Panizio and coworkers from Portugal and Brazil, Izquierdo and Lopez-Coca and coworkers from Spain, Vinitnantharat and coworkers from Thailand and the UK, Roy and Fini and coworkers from India and the USA, Lawson and Luyima and coworkers from Ghana and South Korea, Fouad El Mansouri and coworkers from Morocco and Spain, and Man Zhang and Yaning Zhang and coworkers from China for the 10 scholarly research papers dealing with the synthesis, characterization, properties, and applications of new carbon materials. The applications are mainly centered around environmental remediation owing to the intensity of the problem and comprise of water decontamination and the removal of heavy metals and organic compounds like tannic acid from contaminated sources. The use of biochar in agriculture for preventing nitrogen leaching for soil has also been included. Unconventional carbon-related materials like rice husk ash and carbon steel slag were also used for exotic applications.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.077 |
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".