{"id":"W4415423483","doi":"10.1016/j.immuno.2025.100062","title":"Machine learning in AIRR diagnostics: Advances and applications","year":2025,"lang":"en","type":"article","venue":"ImmunoInformatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome Canada; Simon Fraser University","funders":"U.S. National Library of Medicine; National Institute of Allergy and Infectious Diseases; Deutsche Forschungsgemeinschaft","keywords":"Focus (optics); State (computer science); Training set; Repertoire; Active learning (machine learning)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007384591,0.00006669483,0.00009928352,0.0001048545,0.00004756559,0.00003413831,0.00005472618,0.00003405842,0.000004301517],"category_scores_gemma":[0.00003234838,0.00006629004,0.00001346224,0.0001645262,0.00001334976,0.00015394,0.00001507576,0.0001390872,0.00002595647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002800312,"about_ca_system_score_gemma":0.000006459483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001189835,"about_ca_topic_score_gemma":0.00002882606,"domain_scores_codex":[0.9995904,0.000006309428,0.0002309547,0.00003268823,0.00004254926,0.00009710681],"domain_scores_gemma":[0.9998025,0.00008052516,0.00001947907,0.00007209068,0.0000107451,0.000014616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001052784,0.00002161777,0.005919246,0.001265071,0.00006201341,0.000001273199,0.001404287,0.1515376,0.0002152574,0.005605371,0.0002695556,0.8336882],"study_design_scores_gemma":[0.0003397207,0.000007644106,0.0008313071,0.00005220862,0.000004667253,0.000002686832,0.0004366118,0.5332363,0.00005764506,0.0002012858,0.4647551,0.00007486649],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03907648,0.2127318,0.3152201,0.000322903,0.001484922,0.00238441,0.00003521789,0.0024731,0.4262711],"genre_scores_gemma":[0.992568,0.00649776,0.0003077846,0.00006209079,0.00001623642,0.0001358503,0.000006591723,0.00000777468,0.0003979559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9534914,"threshold_uncertainty_score":0.2703229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002161965810614259,"score_gpt":0.2037635460223367,"score_spread":0.2016015802117225,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}