A brief bibliometric analysis of Web of Science publications on “Carbon” topic for 2019–2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.056 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it