Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for the Neuropsychobiology:Izel Cemre Aksahin, Zeytinburnu, TurkeyDina Ali, Rochester, MN, USAHidayet Ece Arat-Çelik, Istanbul, TurkeyGörkem Ayas, Istanbul, TurkeyYavuz Ayhan, Ankara, TurkeyÇiçek nur Bakır, Rochester, MN, USASinem Balaç, Istanbul, TurkeySibel Çakır, Istanbul, TurkeyRaffaella Calati, Milan, ItalyGiovanni Camardese, Rome, ItalyGünes Sayan Can, Karataş, TurkeyHao Chen, Dresden, GermanyNorberto Cysne Coimbra, Ribeirão Preto, BrazilCemal Demirlek, Boston, MA, USAJulia Diemer, Munich, GermanyEva Döring-Brandl, Berlin, GermanyAntonio Drago, Aalborg, DenmarkOnur Durmaz, Istanbul, TurkeySultan Ekinci, Istanbul, TurkeyMete Ercis, Rochester, MN, USACagatay Ermis, Gothenburg, SwedenEmre Cem Esen, İzmir, TurkeyAysan Eslami-Abriz, İstanbul, TurkeyChiara Fabbri, Bologna, ItalyAndrea Fagiolini, Siena, ItalyZahra Farahnak, St. John's, NL, CanadaAitak Farzi, Graz, AustriaMoncef Feki, Tunis, TunisiaIbrahim Fettahoğlu, Malatya, TurkeyAlexander Finner, Graz, AustriaDiego Forero, Bogotá, ColombiaGernot Fugger, Sankt Pölten, AustriaThiago Guimarães, Niteroi, BrazilEzgi Ince Guliyev, Istanbul, TurkeyAhmet Gürcan, Ankara, TurkeyAna G. Gutiérrez-García, Xalapa, MexicoKenji Hashimoto, Chiba, JapanAlfred Häussl, Graz, AustriaUrs Heilbronner, Munich, GermanyKoichi Hirata, Mibu, JapanRoxane Hoyer, Quebec, QC, CanadaWei-Lieh Huang, Douliu, TaiwanRifat Serav İlhan, Ankara, TurkeyRyouhei Ishii, Habikino, JapanHasan Kazdağlı, İzmir, TurkeyYunna Kim, Seoul, Republic of KoreaBurcu Kök Kendirlioğlu, Istanbul, TurkeyUmut Kökbaş, Nevşehir Merkez, TurkeyMikael Landén, Gothenburg, SwedenMarko Martinac, Maršala Tita, Bosnia and HerzegovinaCeren Meriç, Istanbul, TurkeyPrzemyslaw Mikolajczak, Poznań, PolandPavol Mikolas, Dresden, GermanyEmre Mısır, Ankara, TurkeyS.D. Moore, Durham, USAAisha Judith Leila Munk, Giessen, GermanyPetra Netter, Marburg, GermanyAysegul Ozerdem, Rochester, MN, USAGustavo Padron-Rivera, Tlahuelilpan, MexicoRay Paloutzian, Santa Barbara, CA, USAJean-Michel Petot, Paris, FranceRobert Queissner, Graz, AustriaKaitlin Roke, Kelowna, BC, CanadaJanusz K. Rybakowski, Poznan, PolandYasaman Saba, Graz, AustriaBegüm Şahbudak, Manisa, TurkeyGerold Schratt, Graz, AustriaOmar Sery, Brno, CzechiaMaureen Smeets-Janssen, Amersfoort, The NetherlandsAlessio Squassina, Cagliari, ItalyAxel Steiger, Kaiserslautern, GermanyMarko Stijic, Graz, AustriaLut Tamam, Adana, TurkeyRukiye Tekdemir, Konya, TurkeyElizabeth Thomas, Irvine, CA, USAPeter Thompson, El Paso, TX, USAAdelina Tmava-Berisha, Graz, AustriaWen-Jun Tu, Beijing, ChinaZgür Tunçel, Samsun, TurkeySnezana Urosevic, Minneapolis, MN, USASabide Duygu Uygun, Ankara, TurkeyMilenna van Dijk, New York, NY, USAMaj Vinberg, Copenhagen, DenmarkHong-xing Wang, Beijing, ChinaWalid Yassin, Boston, MA, USABuket Yeşiloğlu, Zeytinburnu, TurkeyMasafumi Yoshimura, Hirakata, JapanKejin Zhang, Xi’an, China
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 | 0.021 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.286 | 0.193 |
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".