Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Journal of Innate Immunity:Gesa Albers, Melbourne, VIC, AustraliaIker Aramburu, Umea, SwedenJohn P. Atkinson, St. Louis, MO, USARory Baird, Dublin, IrelandAnnelie Brauner, Stockholm, SwedenJeremy Brown, London, UKAlison Carey, Brisbane, QLD, AustraliaWilliam Carson, Columbus, OH, USAMarco A. Cassatella, Verona, ItalyJean-Marc Cavaillon, Paris, FranceZissis Chroneos, Hershey, PA, USAMarguerite Clyne, Dublin, IrelandNicolas Cuburu, Bethesda, MD, USAGuilherme de Paula, Paraná, BrazilUlrich Dobrindt, Munich, GermanyHazel M. Dockrell, London, UKAnna Dziuba, Stockholm, SwedenArne Egesten, Lund, SwedenBernd Engelmann, Munich, GermanyScott Evans, Houston, TX, USAGuido Ferlazzo, Messina, ItalyChristine Gaboriaud, Villeurbanne, FranceFu Gao, New Haven, CT, USAJu Gao, Tucson, AZ, USAPeter Garred, Copenhagen, DenmarkDebananda Gogoi, Dublin, IrelandFernando Guimaraes, Brisbane, QLD, AustraliaRobert E. Guldberg, Eugene, OR, USAChristian Herr, Homburg, GermanyHideyo Hirai, Hachioji, JapanJaime Hook, New York, NY, USAAnne Hosmalin, Paris, FranceSalvador Iborra, Alcala de Henares, SpainKerstin Jurk, Mainz, GermanyKevin Kavanagh, Maynooth, UKScott Kobayashi, Hamilton, OH, USAPanagiotis Koufargyris, Athens, GreeceHongrui Li, Chapel Hill, NC, USALin Lin, Shanghai, ChinaGaurav Kumar Lohia, New York, NY, USAJolanta Lukasiewicz, Wroclaw, PolandDaichi Maeda, Kanazawa, JapanJohn K. McCormick, London, ON, CanadaPaola Migliorini, Pisa, ItalySonal Sekhar Miraj, Udupi, IndiaAshu Mohammad, Stanford, CA, USABrenda Morris, Dublin, IrelandAllan Mowat, Glasgow, UKPhilip Murphy, Bethesda, MD, USADmitry Namgaladze, Frankfurt, GermanyEoghan O’Neill, Dublin, IrelandMiguel A. Ortega, Alcalá de Henares, SpainJong-Hwan Park, Gwangju, Republic of KoreaClaire Pearson, Oxford, UKFilipe S. Pereira-Dutra, Rio de Janeiro, BrazilLucille Rankin, Melbourne, VIC, AustraliaLang Rao, Tianjin, ChinaThierry Roger, Lausanne, SwitzerlandUte Römling, Stockholm, SwedenJohn Charles Rotondo, Ferrara, ItalyTristram Ryan, Boston, MA, USABettina Schock, Belfast, UKDongyan Song, Houston, TX, USAWenchao Song, Philadelphia, PA, USAOle E. Sørensen, Lund, SwedenOlle Stendahl, Linköping, SwedenAtsushi Tabata, Tokushima, JapanRichard Unwin, Manchester, UKConstantin Felix Urban, Umeå, SwedenJudith Voynow, Richmond, VI, USARidhima Wadhwa, Newark, NJ, USAYinsheng Wan, Providence, RI, USAXi Wang, Boston, MA, USAYuedan Wang, Nanjing, ChinaWenjun Wang, Xian, ChinaTheresa L. Whiteside, Pittsburgh, PA, USAHong-Bo Xin, Nanchang, ChinaZhaoyu Xue, Grand Rapids, MI, USAAo Zhang, Shanghai, ChinaDekai Zhang, Texarkana, TX, USAChunfu Zheng, Calgary, AB, CanadaLi Zhou, Wuhan, 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 | 0.020 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.281 | 0.181 |
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".