<scp>NSERC</scp> discovery grants and a tribute to Leo A. Behie
Bibliographic record
Abstract
Abstract The scope of chemical engineering continues to evolve and has become ever more multifaceted. It encompasses traditional fields like polymers, thermodynamics, transport processes, control, and environmental science, but has expanded to include biotechnology, biomedical, food technology, materials, and numerical techniques like artificial neural networks and discrete element methods. In fact, the Natural Sciences and Engineering Research Council of Canada (NSERC) combines chemical engineering and materials in the same evaluation group to award competitive funding for the Discovery Grant. Prof. Leo Behie's career reflected the change in the scope of chemical engineering. In the 1970s and early 1980s, Prof. Behie's research included hydrodynamics of fluidized beds and deriving the kinetics of the Claus reaction. In the late 1980s and into the 1990s, he contributed to biotechnology. In the 2000s and 2010s, his areas of expertise related to biomedical engineering comprising stem cell research and a focus on Parkinson's and Huntington's diseases. The common denominator for most of his research was reactor technology. In 2014, Prof. Behie quipped that chemical engineering was dead and many North American departments shared this tenet and added various scientific fields to recognize the emerging synergies. Here, we show that chemical engineering is a vibrant and growing field. We demonstrate in what way it is changing and describe how NSERC evaluates research dossiers for the Discovery Grants. We describe the relationship between the h index and the number of citations and confirm that bibliometrics has only a minor role in the Discovery Grant award.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".