Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Cardiorenal Medicine.Dmitry Abramov, Loma Linda, CA, USADeepak Acharya, Tucson, AZ, USAIbrahim Akin, Mannheim, GermanyGulali Aktas, Bolu, TurkeyAli AlSahow, Al Jahra, KuwaitEduardo R. Argaiz, Tlalpan, MexicoSudarshan Balla, Morgantown, WV, USAGiovanni Barbati, Vicenza, ItalyClemente Barron, Tlalpan, MexicoAmer Belal, Gainesville, FL, USAClaudia Benedetti, Vicenza, ItalyJiashen Cai, Singapore, SingaporeJesus Casado, Getafe, SpainVipanpreet Chahil, Modesto, CA, USAKristina Charaya, Moscow, RussiaJonathan S. Chavez Iñiguez, Guadalajara, MexicoMeihua Chen, Haikou, ChinaJi-dong Cheng, Xiamen, ChinaAna Cruz Solbes, Boston, MA, USAGianpiero Damico, Venice, ItalyRafael de la Espriella, Valencia, SpainPaul Der Mesropian, Alabany, NY, USAAbhishek Deshpande, Bengaluru, IndiaJavier Diez, Pamplona, SpainYiHong Du, Shanghai, ChinaRobert Ekart, Maribor, SloveniaGuido Filler, Toronto, ON, CanadaMatteo Floris, Cagliari, ItalyJara Gayán Ordás, Lleida, SpainJared Gollie, Washington, DC, USAMiguel González Rico, Valencia, SpainXiangchen Gu, Shanghai, ChinaGregor Guron, Gothenburg, SwedenAbiodun Idowu, Philadelphia, PA, USAVictor Issa, Antwerp, BelgiumJuan B. Ivey-Miranda, Mexico City, MexicoGuang Ji, Shanghai, ChinaGuanghong Jia, Columbia, MO, USAKyung An Kim, Seoul, Republic of KoreaBernd Krüger, Darmstadt, GermanyAlexander Kula, Chicago, IL, USALalathaksha Kumbar, Detroit, MI, USAPau Llacer, Madrid, SpainKunal Malhotra, St. Louis, MO, USAShreepriya Mangalgi, Columbia, MO, USAPedro Marques, Porto, PortugalMarco Mojoli, Pordenone, ItalyShan Mou, Shanghai, ChinaMelin Narayan, Loma Linda, CA, USAJacob Ninan, Olympia, WA, USARavi Nistala, Columbia, MO, USAGonzalo Núñez-Marin, Valencia, SpainJaume Padilla, Columbia, MO, USAAlberto Palazzuoli, Siena, ItalySuchita Pande, Boston, MA, USAXavier Fernando Parada, Worcester, MA, USAAlex Parker, Gainesville, FL, USAJose Angel Perez Rivera, Burgos, SpainAntonino Previti, Santorso, ItalyAbd Qannus, Tucson, AZ, USAAfif Ramadhan, Sleman, IndonesiaZaccaria Ricci, Florence, ItalyHongliang Rui, Beijing, ChinaCrina Claudia Rusu, Cluj-Napoca, RomaniaAbhishek Samprathi, Bengaluru, IndiaSarah Sanghavi, Seattle, WA, USANagaraju Sarabu, Cleveland, OH, USAKristin Schreiner, Heidelberg, GermanyDavid Selewski, Charleston, SC, USAYunfeng Shen, Shenzhen, ChinaRyann Sohaney, Ann Arbor, MI, USAKsenija Stach, Mannheim, GermanyCynthia Ann Stavish, Oak Ridge, TN, USAMingliang Tang, Nanjing, ChinaFei Tong, Xiamen, ChinaCundullah Torun, Istanbul, TurkeyJunior Uduman, Detroit, MI, USASerkan Ünlü, Ankara, TurkeyJorge Vazquez Lopez-Ibor, Madrid, SpainGiovanni Vescovo, Mestre, ItalyKrishnaswami Vijayaraghavan, Phoenix, AZ, USABenjamin Wagner, Detroit, MI, USAMatthew R. Weir, Baltimore, MD, USAR. Scott Wright, Rochester, NY, USAXiaohong Wu, Zhejiang, ChinaI-Wen Wu, Taipei, TaiwanPreethi Yerram, Columbia, MO, USADong-Dong Yu, Guangzhou, ChinaChen Yu, Shanghai, ChinaXiaohui Zhao, Chongqing, ChinaLiang Zheng, Shanghai, ChinaJiahua Zhou, Wenzhou, ChinaLara Zonneveld, Groningen, The Netherlands
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".