Bibliographic record
Abstract
The Journal of Experimental Orthopaedics is very fortunate to have excellent reviewers who kindly agree and spend their time reviewing manuscripts for the journal. Our reviewers work hard to ensure a rapid, rigorous and fair peer review process of each manuscript. Such peer review, focusing solely on the scientific integrity to determine the quality of the research submitted, is very critical to the success of our young journal. I wish to thank very much those reviewers who provided their time and expertise in evaluating manuscripts for the Journal of Experimental Orthopaedics in 2014/2015. Sincerely, Henning Madry Editor in Chief Reviewers 2014/2015 Ashraf Abdelkafy, Ismailia, Egypt Roy Altman, Los Angeles, United States of America Olufemi Ayeni, Hamilton, Canada Magali Cucchiarini, Homburg, Germany Nikica Darabos, Zagreb, Croatia Laura De Girolamo, Milan, Italy Patricia Diaz Rodriguez, Troy, United States of America Sabrina Ehnert, Tubingen, Germany Muhammad Farooq Rai, St Louis, United States of America Liang Gao, Homburg, Germany Alan Getgood, Cambridge, United Kingdom Lars Goebel, Homburg, Germany Enrique Gomez-Barrena, Madrid, Spain Tobias Gotterbarm, Heidelberg, Germany Sibylle Grad, Davos, Switzerland Farshid Guilak, Durham, United States of America Alan Hargens, San Diego, United States of America Emily Hu, Palo Alto, United States of America Clark Hung, New York, United States of America Christof Hurschler, Hannover, Germany Tao Ke, Homburg, Germany Tomonori Kenmoku, Kanagawa, Japan Clemens Koesters, Munster, Germany Elizaveta Kon, Bologna, Italy Sebastian Kopf, Berlin, Germany Alexis Lion, Luxembourg, Luxembourg Christopher Little, St Leonards, Australia Punyawan Lumpaopong, Phitsanulok, Thailand Marina Macias-Silva, Mexico D. F., Mexico Laurent Malisoux, Luxembourg, Luxembourg Hermann Mayr, Munchen, Germany Babak Moradi, Heidelberg, Germany Caroline Mouton, Strassen, Luxembourg Jason Mussell, New Orleans, United States of America Koichi Nakagawa, Sakura-shi, Chiba, Japan Norimasa Nakamura, Osaka, Japan Sven Nebelung, Aachen, Germany Geoffroy Nourissat, Paris, France Patrick Orth, Homburg, Germany Dietrich Pape, Luxembourg, Luxembourg Stephen Parada, Fort Gordon, United States of America Silvia Pianigiani, Milan, Italy Bernd Rolauffs, Tubingen, Germany Claudio Rosso, Basel, Switzerland Ryan Rubin, New Orleans, United States of America Gian Salzmann, Zurich, Switzerland Jose Sanhudo, Porto Alegre, Brazil John Segreti, Chicago, United States of America Romain Seil, Luxembourg, Luxembourg Lori Setton, Durham, United States of America Lazar Sijak, Belgrade, Serbia Daniel Theisen, Luxembourg, Luxembourg Jordan Trafimow, Chicago, United States of America Ronald van Heerwaarden, Woerden, The Netherlands Francesca Vannini, Bologna, Italy Peter Verdonk, Antwerp, Belgium William Walsh, Randwick, Australia
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 | 0.018 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.257 | 0.234 |
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".