Acknowledgment to the Reviewers
Bibliographic record
Abstract
Good reviewers are essential to the success of any journal and peer review is a major pillar of science. We are grateful to those mentioned below to have dedicated their time and expertise to help our authors improve and refine their manuscripts and support the Editors in the decision making process in the past year. Adamek Henning, Germany Aerts Johannes M.F.G., The Netherlands Akbari Hamed, Canada Artunc Ferruh, Germany Auernhammer Christoph, Germany Bader Michael, Germany Badr Gamal, Egypt Bartelt Alexander, Germany Baumann Anja, Austria Bianco Valentina, Austria Biester Torben, Germany Bodilsen Stefan Sjørslev, Denmark Boettcher Anika, Germany Bojunga Jörg, Germany Burkart Volker, Germany Case Adam J., Texas Chen Fang, China Chottova Dvorakova Magdalena, Czech Republic Ciccodicola Alfredo, Italy Dascal Juliana Bayeux, Brazil Detomas Mario, Germany Di Dalmazi Guido, Italy Dischinger Ulrich, Germany Dobrijevic Anja, Austria Ergün Süleyman, Germany Fuss Carmina, Germany Galli-Tsinopoulou Assimina, Greece Gallwitz Baptist, Germany Gancheva Sofiya, Germany Grassiolli Sabrina, Brazil Gruppetta Mark, Malta Haak T., Germany Hackett Geoff, United Kingdom Hocher Berthold, Germany Holder M., Germany Honegger Jürgen, Germany Igaz Peter, Hungary Jarzab Barbara, Poland Jebasingh Felix, India Jiang Nan, China Johansson Jonathan, Sweden Kann P., Germany Kapellen Thomas, Germany Kender Zoltan, Germany Kline Gregory A., Canada Koch Cristian, Germany Köhler Viktoria, Germany Köhrle Josef, Germany Koschker Ann-Cathrin, Germany Kroiss Matthias, Germany Kürzinger Lydia, Germany Lange Karin, Germany Laudes Matthias, Germany Losa Marco, Italy Lottspeich Christian, Germany Macut Djuro, Serbia McCabe Christopher J, United Kingdom Medina Reinhold, United Kingdom Moreira Veridiana, Brazil Müller-Wieland Dirk, Germany Nabeh Omnia Azmy, Egypt Onogi Yasuhiro, Japan Papanas Nikolaos, Greece Parhofer Klaus, Germany Paschou Stavroula, Greece Pepe Jessica, Italy Pilz Stefan, Australia Pinzon Rizaldy Taslim, Indonesia Popovic Djordje, Serbia Rainey William, Michigan Reincke Martin, Germany Ruiz-Ojeda Francisco, Spain Rustenbeck I., Germany Sabino-Carvalho Jeann L., Brazil Salehi Albert, Sweden Saravanan Ganapathy, India Sbiera Silviu, Germany Schaaf Ludwig, Germany Schilbach Katharina, Germany Schmidmaier Ralf, Germany Schopohl Jochen, Germany Seefried Lothar, Germany Seidensticker Max, Germany Seifert-Klaus Vanadin Regina, Germany Shao Jiaqing, China Sibiya Ntethelelo, South Africa Song Bao-Liang, China Srivastav Shival, India Stefan Norbert, Germany Störmann Sylvère, Germany Stürmer Paula, Germany Suh Jin-Soon, South Korea Tan Susanne, Germany Theodoropoulou Marily, Germany Thienel Florian, Germany Tormey W.P., Irland Ussar Siegfried, Germany Vaidya Anand, Massachusetts Vakili Sina, Iran Valitutti Francesco, Italy Völkl Jakob, Austria Völter Friederike, Germany Wagner Robert, Germany Wan Lu, Pennysilvania Weber Katharina, Germany Yang Jun, Australia Yin Jun, China Zafirovic Sonja, Serbia Zaharia Oana-Patricia, Germany Zara Sandra, Germany Publication History Article published online: 18 January 2024 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".