{"id":"W3128063613","doi":"10.2139/ssrn.3749694","title":"Human Plasma IgG1 Repertoires are Simple, Unique, and Dynamic","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Blood groups and transfusion","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"IONICS Mass Spectrometry (Canada)","funders":"","keywords":"Simple (philosophy); Computer science; Computational biology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002650736,0.0003671894,0.0001819843,0.0004276645,0.0002404775,0.0005392267,0.0003617916,0.0002737467,0.004757457],"category_scores_gemma":[0.0003833634,0.000174582,0.0002013583,0.0002526358,0.0002287085,0.0003386811,0.0005255157,0.0003519878,0.00117666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191488,"about_ca_system_score_gemma":0.0001455349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004433435,"about_ca_topic_score_gemma":0.0005526019,"domain_scores_codex":[0.9998106,0.00003792588,0.000008861965,0.00005943996,0.00003706984,0.00004609946],"domain_scores_gemma":[0.999827,0.00004271631,0.00004399441,0.0000238617,0.00003385712,0.00002863937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006124937,0.00005907086,0.0241999,0.0001655976,0.00009252128,0.0005584533,0.0004010659,0.0006244517,0.903509,0.002717844,0.001528712,0.06553081],"study_design_scores_gemma":[0.0002255639,0.002003433,0.3128093,0.0002368843,0.0004260352,0.01810875,0.001639289,0.01034582,0.51355,0.01745752,0.1230732,0.0001243108],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829123,0.001917856,0.004833846,0.0001950185,0.00004294746,0.00002025593,0.0003904158,0.0001609585,0.009526338],"genre_scores_gemma":[0.9933785,0.0004891986,0.001896559,0.0002095578,0.00003917175,0.00001567669,0.0004152226,0.00004441574,0.003511586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004757457,"threshold_uncertainty_score":0.01591527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00842759442831391,"score_gpt":0.2483666301569743,"score_spread":0.2399390357286604,"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."}}