Bibliographic record
Abstract
Extract Organizers DEVORET Michel Collège de France, Paris, France HUARD Benjamin Université Pierre et Marie Curie, Paris, France SCHOELKOPF Robert Yale University, New Haven, USA CUGLIANDOLO Leticia Université Pierre et Marie Curie, Paris, France Lecturers BLATT Rainer University of Innsbruck, Austria BLOCH Immanuel Max Planck Institute, Garching, Germany BUISSON Olivier Institut Néel, Grenoble, France CHUANG Isaac Massachusetts Institute of Technology, Cambridge, USA CLARKE John University of California, Berkeley, USA CLERK Aashish McGill University, Montreal, Canada ESTEVE Daniel SPEC–CEA Saclay, Gif-sur-Yvette, France GIRVIN Steven Yale University, New Haven, USA HARRIS Jack Yale University, New Haven, USA KOROTKOV Alexander University of California, Riverside, USA LEHNERT Konrad JILA, University of Colorado, Boulder, USA MABUCHI Hideo Stanford University, USA MARQUARDT Florian University of Erlangen, Germany MARTINIS John University of California, Santa Barbara, USA NAKAMURA Yasunobu The University of Tokyo, Japan RAIMOND Jean-Michel LKB–Ecole Normale Supérieure, Paris, France SIDDIQI Irfan University of California, Berkeley, USA WALLRAFF...
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.713 | 0.614 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".