Bibliographic record
Abstract
"Genes, Germs and Medicine explores the development of modern biomedical science in the United States through the life of one of the Twentieth Century's most influential scientists. Joshua Lederberg was a scientific renaissance man. He and his collaborators founded the field of bacterial genetics, and he was awarded the Nobel Prize at the age of 33 (the second youngest in history). He helped to lay the foundations for genetic engineering, made fundamental revisions to immunological and evolutionary theory, and developed medical genetics. He initiated the search for extraterrestrial microbial life, developed artificial intelligence, and was a visionary of the Digital Age. Lederberg coined some of the central terms of modern biology: plasmid, transduction, exobiology, euphenics and microbiome. A complex humanist who spoke out for social justice, Lederberg confronted racism, and denied a gene-centered view of humans. Pondering our social evolution outside of nature, he forewarned of the complex ethical issues arising from bioengineering. He sounded the alarm about coming pandemics at a time when few would listen, and warned of the peril of biowarfare and strove to prevent it. Lederberg was a man with a deep sense of social and intellectual responsibility, a trusted advisor to eight presidential administrations"--Publisher's website
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".