Chemical engineering research synergies across scientific categories
Bibliographic record
Abstract
Abstract Chemical engineers operate industrial plants, design reactors and equipment, manage capital projects, estimate costs, project earnings, and drive efficiency through innovation while maintaining rigorous safety standards. The undergraduate curriculum includes mathematics, physics, chemistry, mechanics, biology, and management, much of which is common with other engineering departments. However, chemical engineering research is more related to chemistry. Here, we show that chemical engineers cite journals in WoS’ chemical engineering category most, followed by physical chemistry, energy & fuels, multi‐disciplinary chemistry, environmental science, and multi‐disciplinary materials science. According to a bibliometric analysis, the major research poles include materials, biotechnology, catalysis, environment, and thermodynamics. The 5 top cited journals in 2012 were Ind. Eng. Chem. Res., J. Membrane Sci., Chem. Eng. Sci., J. Hazard. Mater., and J. Catal., which are not the journals with the highest impact factors of the category. Can. J. Chem. Eng. was ranked third among the 32 classical chemical engineering journals after AIChE J. and Chem. Eng. Sci. and with respect to the ratio of the number of citations accrued until August 2017 to the number of articles they published in 2012. Chinese researchers have authored more articles than any other nation and they co‐author research most with the USA and other Pacific Rim nations. Research collaborations between nations follow linguistic, geographical, and historical traditions.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.086 | 0.097 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".