Is bigger better? Modeling AAV production to find optimization opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Watch the video or view the poster for insights into:Defining key cost drivers in AAV manufacturingEmploying bioprocess modeling to determine the greatest influencers of cost driversHow bioprocess modeling helps to inform and narrow decision makingFinancial, global, and environmental impacts of AAV manufacturing process changesAndrea Vervoort is a scientific professional with experience in the fields of bioprocess engineering and gene therapy production. As a Technical Lead at Virica Biotech, she is a subject matter expert in viral sensitizer technology, and is responsible for providing technical guidance and support during client evaluations of Virica’s technology. She also develops data driven models of gene therapy production processes that provide strategic insight into process optimization.Prior to joining Virica in 2020, Andrea was with the Ottawa Hospital Research Institute as a Cancer Therapeutics Research Assistant in the lab run by Dr Jean-Simon Diallo, now CEO and Scientific Founder of Virica Biotech. She holds a BAS in Chemical Engineering from Queen’s University, with a concentration in biochemical engineering.
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.
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 | 0.000 |
| 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 | 0.000 |
| 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 it