Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for the ongoing support in reviewing manuscripts for Journal of Innate Immunity:Danielle Ahn, New York, NY, USADilara Akbulut, Bethesda, MD, USAStella E. Autenrieth, Heidelberg, GermanyLuis Alberto Baena Lopez, Oxford, UKRory Baird, Dublin, IrelandJoshua Benoit, Cincinnati, OH, USACaroline Bergenfelz, Lund, SwedenFabien Blanchet, Montpellier, FranceCatharine M. Bosio, Hamilton, MT, USAMalgorzata Bzowska, Krakow, PolandVinicius Carvalho, Rio de Janeiro, BrazilPallavi Chandra, St. Louis, MO, USAZissis Chroneos, Hershey, PA, USAAndrew Currie, Perth, WA, AustraliaLeonard de Boer, Stockholm, SwedenFrank R. DeLeo, Hamilton, OH, USAKarim Dib, Belfast, UKSarah Dimeloe, Birmingham, UKGeorge R. Dubyak, Cleveland, OH, USAShailendra Dwivedi, Gorakhpur, IndiaSteven Edwards, Liverpool, UKOskar Eriksson, Uppsala, SwedenMohammed Eslam, Sydney, NSW, AustraliaCongjing Feng, Yangzhou, ChinaLucas Ferrari de Andrade, New York, NY, USAEmily Findlay, Southampton, UKCecilia Garlanda, Rozzano, ItalyMartin Gericke, Leipzig, GermanyDebananda Gogoi, Dublin, IrelandGermán E. González, Buenos Aires, ArgentinaJingang Gui, Beijing, ChinaSabelo Hadebe, Cape Town, South AfricaHani Harb, Dresden, GermanyHideyo Hirai, Hachioji, JapanPhilipp J. Hohensinner, Vienna, AustriaShoujun Huang, Hefei, ChinaSalvador Iborra, Alcala de Henares, SpainVittoria Infantino, Potenza, ItalyMalay Jhancy, Ras Al-Khaimah, United Arab EmiratesEleni Karakike, Athens, GreeceAntigoni Kotsaki, Athens, GreecePanagiotis Koufargyris, Athens, GreeceIoannis Kourtzelis, York, UKDorothee Kretschmer, Tübingen, GermanyMohammad Shafi Kuchay, Gurugram, IndiaHrishikesh Kulkarni, St. Louis, MO, USAEvdoxia Kyriazopoulou, Athens, GreeceKonstantinos Leventogiannis, Athens, GreeceYingxi Li, Houston, TX, USAXueqing Li, Bethesda, MD, USAHongrui Li, Madison, WI, USAFengqi Li, Hefei, ChinaXiaofei Li, Shanghai, ChinaXinhua Liu, Hangzhou, ChinaCatherine Loynes, Sheffield, UKChristine Luttermann, Greifswald, GermanyCoralie Martin, Paris, FranceDerek M. McKay, Calgary, AL, CanadaKristin Michel, New York, NY, USAErik Michels, Amsterdam, The NetherlandsThomas Miethke, Mannheim, GermanyIoannis Mitroulis, Alexandroupolis, GreeceAllan Mowat, Glasgow, UKVictoriano Mulero, Murcia, SpainPhilip Murphy, Bethesda, MD, USARajagopal Murugan, Leiden, The NetherlandsMarco Rinaldo Oggioni, Bologna, ItalyCarlos Orihuela, Birmingham, AL, USAPraveen Papareddy, Lund, SwedenClaire Pearson, Oxford, UKFilipe S. Pereira-Dutra, Rio de Janeiro, BrazilJenny J. Persson, Lund, SwedenAline Pfefferle, Stockholm, SwedenAlessandro Poggi, Genoa, ItalyAlice Prince, New York, NY, USALeah M. Pyter, Columbus, OH, USALang Rao, Tianjin, ChinaJens Rolff, Berlin, GermanyGuzman Sanchez-Schmitz, Boston, MA, USAKatrin Schaper-Gerhardt, Hannover, GermanyMark Schembri, Brisbane, QLD, AustraliaOonagh Shannon, Malmö, SwedenWei Shi, Boston, MA, USATomasz Skirecki, Warsaw, PolandBarbara Spellerberg, Ulm, GermanyW. Edward Swords, Tuscaloosa, AL, USAFrancesco Tedesco, Milan, ItalyPuck van Kasteren, Bilthoven, The NetherlandsDavid Vermijlen, Brussels, BelgiumSarah Walmsley, Edinburgh, UKYinsheng Wan, Providence, RI, USAXiaoqian Wang, Nanjing, ChinaHaiming Wang, Zhengzhou, ChinaJoanna Wietrzyk, Wroclaw, PolandTania Wong, New York, NY, USAHong-Bo Xin, Nanchang, ChinaZhaoyu Xue, Grand Rapids, MI, USATengbo Yu, Shandong, ChinaLi Zhang, Shenzhen, ChinaWenping Zhou, New Haven, CT, USAZhen Zou, Beijing, 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 | 0.017 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.308 | 0.203 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".