Analysis for Science Librarians of the 2013 Nobel Prize in Physiology or Medicine: The Work of J.E. Rothman, R.W. Schekman, and T.C. Südhof
Bibliographic record
Abstract
The traffic inside a single cell has been described as “… complicated as rush hour near any metropolitan area” (Howard Hughes Medical Institute 2013a). What this year’s three winners of the Nobel Prize in Physiology or Medicine have done is describe how molecules are able to read molecular traffic signs, enabling them to navigate the heavy intracellular traffic—a fundamental process in cellular physiology (Howard Hughes Medical Institute 2013a). This article gives an overview of the work of James Rothman, Randy Schekman, and Thomas Südhof, the 2013 Nobel laureates in Physiology and Medicine.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.055 |
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".