A perspective on <i>The Canadian Journal of Chemical Engineering</i> commemorating its 100th volume: 1929–2021
Bibliographic record
Abstract
Abstract To celebrate the 100th volume of The Canadian Journal of Chemical Engineering ( CJCE ) in 2022, we briefly narrate its history and accomplishments. The CJCE 's journey began in 1929 with the launch of the Canadian Journal of Research ( CJR ), which transformed to the Canadian Journal of Technology ( CJT ) in 1951, and finally to its present name in 1957 as the flagship publication of the Canadian Society for Chemical Engineering (CSChE). Using statistical data and keywords mined from Clarivate's Web of Science (WoS) together with manual searches of the articles published in the CJR and CJT , we describe how the scope of chemical engineering has continued to evolve, over the past 90+ years, in becoming ever more multifaceted. Chemical engineering encompasses traditional areas, such as polymers, thermodynamics, transport phenomena, transfer and separation processes, reactor design, energy conversion, process simulation and control, and environmental science; however, it has been expanding to include biotechnology, biomedical, food processing, novel composite materials, nanotechnology, renewable/green energy, CO 2 capture and transformation, and numerical techniques like neural networks, artificial intelligence, discrete element methods, etc. Like all scientific journals, the growth and success of the CJCE are attributed to the commitment of its contributing authors. We recognize and celebrate the contributions of several prominent Canadian and international researchers, who published their articles in the CJCE . With a number of new initiatives launched in the last decade, we foresee continued improvements in the stature of the CJCE as a top‐ranked journal for publishing impactful research, leading to advancements in chemical sciences and engineering.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".