Bibliographic record
Abstract
The 2023 Nobel Prize in Physiology and Medicine was awarded jointly to Katalin Karikó and Drew Weissman for their contributions to the development of mRNA vaccines against COVID-19. Dr. Karikó obtained her doctoral degree from the University of Szeged before moving to the United States and obtaining the position of an adjunct professor at the University of Pennsylvania.1 Dr. Weissman obtained an M.D. and a Ph.D. degree at Boston University and subsequently obtained a faculty position at the Perelman School of Medicine at the University of Pennsylvania. The two researchers met during their shared time at the University of Pennsylvania and collaborated in exploring mechanisms in which mRNA can be used to stimulate immunity development in the body.2 By modifying nucleotides in mRNAs, the pair discovered that the subsequent introduction of mRNAs into cells led to reduced inflammatory responses and increased immune protein production. Before the COVID-19 pandemic, most vaccines stimulated immune responses via attenuated viruses, proteins, or viral genetic code-carrying vectors. These vaccines, while effective, required substantial resources to produce the number of cell cultures needed to synthesize adequate supplies of vaccine to combat pandemics, such as COVID-19.3 Contrastingly, mRNA vaccines can be prepared without cell cultures through in vitro transcription and nucleotides can be modified to adapt to mutating pathogens - flexibilities that allowed more than 12 billion COVID-19 vaccines to be produced and administered in less than three years of the start of the pandemic.4,5
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.204 | 0.126 |
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".