Research contributions of Leo A. Behie to chemical and biomedical engineering
Bibliographic record
Abstract
Abstract Leo Augustus Behie was known as a dedicated teacher and mentor, a team builder, and an outstanding researcher and innovator. He had a most productive and illustrious career, spanning five decades, that covered both industrial and academic sectors. His academic research career started with tackling classical chemical engineering challenges and, with time, shifted to biotechnology and biomedical engineering. He was the founding director of a unique research laboratory for undertaking innovative biotechnology and biomedical engineering research at the University of Calgary, called the Pharmaceutical Production Research Facility (PPRF). This paper presents an overview of his academic and research accomplishments, which also included the training of 17 doctoral students, 21 master's degree students, and many postdoctoral scholars. His main research areas under the classical chemical engineering theme included the investigation of the grid region in gas–solid fluidized bed reactors; gas–liquid and liquid–solid multiphase systems; fluidized bed reactors for metals recovery from coal ash; spouted fluid‐bed reactors; kinetics of pyrolysis chemical reactions; transport phenomena in rotary drums; and reactions, thermodynamics, and reactor modelling of sulphur systems. His main research areas under the biotechnology and biomedical engineering theme included advances in biotechnology and bioreactor design; production of antibiotic and monoclonal antibody; protein production in insect cells; process development for the production of neural stem cells, cancer stem cells, and islet cells; mesenchymal stem cells; and cellular secretome studies aimed at developing a cure for Parkinson's disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".