Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Glomerular Diseases:Lama Abdelnour, Los Angeles, CA, USAVinita Agrawal, Lucknow, IndiaSyeda Behjat Ahmad, Seattle, WA, USAShreeram Akilesh, Seattle, WA, USAPooja Amarapurkar, Pittsburgh, PA, USANicole K. Andeen, Portland, OR, USANayan Arora, Portland, OR, USAAmbarish Athavale, San Diego, CA, USARupali S. Avasare, Portland, OR, USABryce Barr, Winnipeg, MB, CanadaMoumita Barua, Toronto, ON, CanadaShane A. Bobart, Houston, TX, USARaed Bou Matar, Cleveland, OH, USABenoit Brilland, Angers, FranceLihong Bu, Rochester, NY, USAMartin Busch, Jena, GermanyPietro A. Canetta, New York, NY, USACaitlin Carter, San Diego, CA, USASimon Carter, Melbourne, VIC, AustraliaDawn J. Caster, Louisville, KY, USATiffany N. Caza, Little Rock, AR, USAAnthony Chang, Chicago, IL, USAAnand Chellappan, Nagpur, IndiaHae Yoon Grace Choung, West Hollywood, CA, USACláudia Costa, Lisbon, PortugalTiane Dai, Torrance, CA, USAKaren De Wolski, Seattle, WA, USALucia Del Vecchio, Como, ItalyPierre Delanaye, Liège, BelgiumPasquale Esposito, Genoa, ItalyShivani Garg, Madison, WI, USAPhilipp Gauckler, Innsbruck, AustriaDorey A. Glenn, Chapel Hill, NC, USASander Groen in’t Woud, Maastricht, The NetherlandsMark Haas, Los Angeles, CA, USAMatthew Hall, Nottingham, UKRamy Hanna, Irvine, CA, USAJean Hou, Los Angeles, CA, USASimon Hsu, Seattle, WA, USAVanja Ivkovic, Zagreb, CroatiaRichard Johnson, Aurora, CO, USARenate Kain, Vienna, AustriaNeeraja Kambham, Stanford, CA, USAElaine S. Kamil, Los Angeles, CA, USAJason Kidd, Richmond, VA, USAVanderlene L. Kung, Portland, OR, USABenjamin Lidgard, Seattle, WA, USACynthia C.W. Lim, Singapore, SingaporeEric Keoni Magliulo, Omaha, NE, USAA. Bilal Malik, Seattle, WA, USAJuan Manuel Mejía, Mexico City, MexicoNidia Messias, St. Louis, MO, USASafak Mirioglu, Istanbul, TurkeyKana N. Miyata, St. Louis, MO, USAJohann Morelle, Namur, BelgiumVanessa Moreno, Chapel Hill, NC, USAAhmet Murt, Adacık, TurkeyCarla Nester, Iowa City, IA, USASayna Norouzi, Loma Linda, CA, USAMadeleine Pahl, Orange, CA, USAMatthew B. Palmer, Philadelphia, PA, USAIoannis Parodis, Stockholm, SwedenAanand Patel, Seattle, WA, USAManuel Praga, Madrid, SpainArun Rajasekaran, Birmingham, AL, USAMichelle Rheault, Minneapolis, MN, USABethany Roehm, Dallas, TX, USABancha Satirapoj, Bangkok, ThailandMårten Segelmark, Lund, SwedenAngel Sevillano, Madrid, SpainGeetika Singh, New Delhi, IndiaSmeeta Sinha, Salford, UKAndrej Skoberne, Ljubljana, SloveniaTarak Srivastava, Kansas City, MO, USAThomas Stehlé, Marne La Vallée, FranceIsaac Ely Stillman, New York, NY, USANicola M. Tomas, Hamburg, GermanyMegan L. Troxell, Stanford, CA, USANina Visočnik, Barcelona, SpainChia-shi Wang, Atlanta, GA, USAMartin Windpessl, Wels-Grieskirchen, AustriaNikki Wyatt, Chapel Hill, NC, USAXinfang Xie, Xian, ChinaKevin Yau, Toronto, ON, CanadaMargaret K. Yu, Palo Alto, CA, USANikola Zagorec, Zagreb, CroatiaIzabela Zakrocka, Pshedmes’tse Vel’ke, PolandBin Zhu, Hangzhou, 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 | 0.025 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.254 | 0.172 |
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".