Bibliographic record
Abstract
Extract A.V. Akimov Ioffe Physical-Technical Institute, 26 Polytechnicheskaya str, 194021, St Petersburg, Russia e-mail: akimov.os@mail.ioffe.ru M.P. Blencowe Department of Physics and Astronomy, Dartmouth College, Hanover, NH 03755, USA e-mail: miles.p.blencowe@dartmouth.edu S.A. Cavill School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK e-mail: stuart.cavill@nottingham.ac.uk L.J. Challis School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK e-mail: lawrie.challis@nottingham.ac.uk W. Dietsche Max-Planck-Institut für Festkörperforschung, Heisenbergstr. 1, 70569 Stuttgart, Germany e-mail: w.dietsche@fkf.mpg.de R. Fletcher Department of Physics, Queen’s University, 99 University Avenue, Kingston, Ontario, K7L 3N6, Canada e-mail: fletcher@physics.queensu.ca P. Hawker School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK e-mail: philip.hawker@nottingham.ac.uk A J. Kent School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK e-mail: anthony.kent@nottingham.ac.uk C.J. Mellor School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK e-mail: chris.mellor@nottingham.ac.uk F.F. Ouali Bookham Technology Plc, Caswell, Towcester, Northamptonshire, NN12 8EQ, UK
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.692 | 0.575 |
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".