Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for the ongoing support in reviewing manuscripts for Gynecologic and Obstetric Investigation:Faruk Abike, Istanbul, TurkeyFatih Aktoz, Istanbul, TurkeyBaydaa Alsannan, Kuwait, KuwaitJoão Alves, Lisbon, PortugalAyush Anand, Dharan, NepalAlessandro Arena, Bologna, ItalyHafiz Muhammad Arsalan, Bishkek, KyrgyzstanNeta Benshalom-Tirosh, Ashdod, IsraelRoss Berkowitz, Boston, MA, USAMaria Mercedes Binda, Bruxelles, BelgiumRosa Amalia Bobadilla-Lugo, Mexico, MexicoGiorgio Bogani, Milan, ItalyGiorgio Bogani, Milan, ItalyPierre-Adrien Bolze, Lyon, FranceByron Calhoun, Charleston, WV, USATommaso Capezzuoli, Florence, ItalyWoraphot Chaowawanit, Bangkok, ThailandSiHyun Cho, Seoul, South KoreaLai Chyong-Huey, Taoyuan, TaiwanNuman Çim, Istanbul, TurkeyCarmine Conte, Rome, ItalyIan Douglas Cooke, Sheffield, UKLeonora Coopmans, Amsterdam, The NetherlandsIlaria Cuccu, Rome, ItalyGiuseppe Cucinella, Palermo, ItalyOttavia D’Oria, Rome, ItalyJ. Oliver Daly, St Albans, VIC, AustraliaJack Darby, Adelaide, SA, AustraliaGeorge Eleje, Nnewi, NigeriaAndrea Etrusco, Palermo, ItalyShangrong Fan, Shenzhen, ChinaAlessandro Favilli, Perugia, ItalyRuth Freeman, New York, NY, USABrecht Geysenbergh, Antwerp, BelgiumTullio Golia DAugè, Rome, ItalyGiovanni Grandi, Modena, ItalyPraveen Guruvaiah, New York, NY, USAIan Hagemann, St. Louis, MO, USAJoe Haydamous, Houston, TX, USAEdgar Hernandez-Andrade, Houston, TX, USAOsamu Wada, Hiraike, Tokyo, JapanAngelo B. Hooker, Zaandam, The NetherlandsWei Huang, Chengdu, ChinaPei Hui, New Haven, CT, USAYevgeniya Ioffe, Loma Linda, CA, USAUlrika Joneborg, Stockholm, SwedenCihan Kaya, Istanbul, TurkeyKhaleque N. Khan, Kyoto, JapanWilliam Kobak, Chicago, IL, USASeung-Yup Ku, Seoul, South KoreaMichael Kunicki, Warszawa, PolandSowjanya Kurakula, Musheerabad, IndiaLindsay Kuroki, St. Louis, MO, USAShengli Li, Shenzhen, ChinaSharon Lie Fong, Leuven, BelgiumJianxiong Long, Nanning, ChinaLena Luyckx, Herent, BelgiumGeoffrey J. Maher, London, UKStephanie Markovina, St. Louis, MO, USALeslie Massad, St Louis, MO, USAEman T Mehanna, Ismailia, EgyptMislav Mikus, Zagreb, CroatiaAntoine Naem, Duisburg, GermanyKaei Nasu, Yufu, JapanGregg Nelson, Calgary, AB, CanadaGeorge Pados, Thessaloniki, GreeceMegh Patel, Ahmedabad, IndiaAlison Premo, Dearborn, MI, USAPaola Quaresima, Catanzaro, ItalyVafa Rahimi-Movaghar, Tehran, IranGaetano Riemma, Naples, ItalyStefania Saponara, Cagliari, ItalyXavier X. Sastre-Garau, Vandoeuvre-les-Nancy, FranceMirte Schaafsma, Amsterdam, The NetherlandsAntonio Schiattarella, Naples, ItalyTeska Schuurman, Amsterdam, The NetherlandsMichael Seckl, London, UKPietro Serra, Cagliari, ItalyEleazar Soto, Houston, TX, USARadmila Sparic, Belgrade, SerbiaTania Giacoma Spedale, Palermo, ItalyLukas Stalpers, Amsterdam, The NetherlandsAlessandro Svelato, Rome, ItalyJessica Termine, ItalyPremal Thaker, St. Louis, MO, USAJohannes Wilhelmus, Trum, Amsterdam, The NetherlandsTogas Tulandi, Montreal, QC, CanadaFilippo Maria, Ubaldi, Rome, ItalyFelipe Vadillo Ortega, Mexico City, MexicoHuub van Rossum, Amsterdam, The NetherlandsNienke van Trommel, Amsterdam, The NetherlandsKo Van der Velden, Amsterdam, The NetherlandsRosa Helena Villalobos-Gómez, Villahermosa, MexicoShuo Wang, Shanghai, ChinaKurt Rodney Wharton, Royal Oak, MI, USAMatt Winter, Sheffield, UKPinar Yalcin Bahat, Istanbul, TurkeyHasan Yüksel, Aydın, TurkeyYing Zhou, Hefei, ChinaXueqiong Zhu, Wenzhou, China
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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 | 0.016 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.301 | 0.204 |
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".