Bibliographic record
Abstract
He is a specialist in process engineering involving particulate systems in reactive and non-reactive environments.He is the Director of the GRTP (Group of Research on Technologies and Processes).The GRTP operates a brand-new R&D and scale-up facility at the Université de Sherbrooke and is financed by both Canadian & Quebec institutional funding and industrial partners such as Rio Tinto Iron & Titane, KWI and Soleno.From May 2008 to March 2021, he was the holder of the Pfizer Industrial Research Chair in Pharmaceutical Processes.The highly successful output of this Chair led to a continuation of this association, which is now focusing on process intensification, analytical technology development and industrial applications.He has numerous collaborations and projects at the national and international level and he served as one of the leaders in Canada's NCE Network BioFuelNet on Biorefining.He is presently the leader of the Canadian side of the project GOLD funded by Horizon 2020 and comprising 18 partners from the EU, China and Canada.He is an expert in the development of new formulations in heterogeneous catalysis as well as catalytic reactor engineering and his R&D work comprises g-lab and kg-lab test rigs and scaleup.He is a co-founder of the company Enerkem Technologies, precursor of Enerkem, a spin-off commercializing technology in the field of energy from renewable resources.He has received many academic and professional awards and he is a world-renowned researcher and technology transfer expert.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.251 | 0.131 |
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".