A brief bibliometric analysis of Web of Science publications on “Carbon” topic for 2019–2020
Bibliographic record
Abstract
A brief bibliometric analysis of 5,000 most cited scientific publications presented in the Web of Science database on the “Carbon” topic for 2019–2020 is done. It is shown that the world’s leading scientific centers of China, the United States, India, South Korea, Japan and Germany, as well as the Russian Academy of Sciences are involved in research on this topic. The following areas of scientific research were dominant: materials science, physical chemistry, nanotechnology, engineering chemistry, applied physics, energy, electrochemistry, ecology, condensed matter physics. The clustering method based on the co-occurrence of the Author Keywords and the Keywords Plus of the Web of Science system revealed six areas of research: 1. catalysis, hydrogen production, carbon materials doped with nitrogen; 2. graphite/graphene-based energy storage systems; 3. sensors and emissions based on carbon quantum dots; 4. nanocomposites and their physical properties; 5. energy consumption and climate change; 6. adsorption and organic pollutants. The author assumes the high potential of research on the co-production of hydrogen and graphite, which may combine the interests of hydrogen energy development and production of new materials.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.095 | 0.141 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".