High-Yield Production Process of Influenza Virus-Like Particles in Human Cells Toward Large-Scale Vaccine Manufacturing
Bibliographic record
Abstract
DEDICATION "To my mom!For all the love, strength and dedication" "A mi amada mami!Por su amor incondicional y por creer siempre en mi" "To my sister and Mahdi for all the love and support!" "Just keep swimming" Dory from.His orientations were always on point even if I could not understand them at the moment, then, it resulted the best for the project.I feel so honored having worked with Amine, his vision, wisdom, endless ideas combined with humbleness make him a great human being.Thanks so much.I would like to thank my supervisor Olivier Henry for orientating me in my beginnings at École Polytechnique de Montréal, for helping me with the courses and for being always available whenever I needed.I learned from him how to be more practical and objective with my work.His advices were priceless for completing this phase of my life.It is difficult to express in words how grateful I am of having the opportunity to work with Dr.Renald Gilbert during these last years.Renald is a brilliant scientist and has been an excellent supervisor and also a friend to me.He has been involved
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".