Bibliographic record
Abstract
Watch the video or read the poster to learn: Scalable stirred-tank reactor train for pluripotent stem cell expansion improves the scale and process of cell therapy manufacturing High-density seed banks can help remove manual manipulation steps, shorten expansion time by 50%, and decouple subsequent suspension differentiation The potential for process correlations of viable cell densities to remove sampling and reduce labor costs and contamination risk Gary Pigeau received his PhD in Biotechnology from Brock University in St. Catharines, Ontario. He completed a postdoctoral fellowship at the University of Alberta, with a visiting scientist appointment at the University of Oxford. Gary moved to private industry in 2008, where his research group focused on process development and scale-up of a proprietary bioprocess technology. He then moved to commercial, large-scale biomanufacturing in 2013. Gary joined Cytiva and the Centre for Advanced Therapeutic Cell Technologies (CATCT) at CCRM in 2016. He serves as Director of the collaborative facility in Toronto, with a focus on biology support for new product introductions, internal technology development projects, and contract process development services in cell and gene therapy.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".