Bibliographic record
Abstract
The 5,320 top cited articles published between 1902 and 2010 in thermodynamic field were identified and characterized using Science Citation Index Expanded. The analyzed aspects covered characteristics of languages, publication years, Web of Science categories, journals, countries/territories, institutions, and authors. These articles were cited a mean number of 210, ranging from 110 to 399 times, with most of the articles in the 1990s and 2000s. Journal of the American Chemical Society was the most productive journal, followed by Journal of Chemical Physics , and Physical Review Letters in 686 journals. Three topmost categories of the 130 Web of Science categories were multidisciplinary chemistry, biochemistry and molecular biology, and physical chemistry. The top cited articles originated from 1,936 institutions of 63 countries. Eight industrial countries: the USA, the UK, Germany, France, Canada, Japan, Italy, and Russia, took the lead with an overwhelming majority (87%), especially about three fifths for the USA. University of California, Harvard University, and Massachusetts Institution of Technology all from the USA led all the institutions. K.S. Pitzer, P.J. Flory (Nobel laureate), and P.A. Kollman advanced the development of thermodynamic field. Moreover, the most influential articles in the history and in the latest year with their citation life cycles were examined to provide some hints for research focuses and trends. Wigner function has been attractive and will probably continue to be popular in the thermodynamic field. Some emerging concerning related to frequency scale factors, OPLS all-atom force field, entanglement between two or more quantum objects, and some softwares including VAMP, NMRPipe, GRASP2, AutoDock, DMol 3 , and Maxent are likely to receive more attention in the near future.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.065 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".