{"id":"W2791965829","doi":"10.3389/fimmu.2018.00460","title":"Strategies for Generating Diverse Antibody Repertoires Using Transgenic Animals Expressing Human Antibodies","year":2018,"lang":"en","type":"article","venue":"Frontiers in Immunology","topic":"Viral Infectious Diseases and Gene Expression in Insects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada)","funders":"","keywords":"Antibody; Antibody Repertoire; Transgene; Immune system; Monoclonal antibody; Genetically modified mouse; Biology; Immunology; Human disease; Computational biology; Genetics; Gene","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":[0.0001401144,0.0002250888,0.0002772,0.0001404935,0.0005498024,0.00007558918,0.0002307827,0.0002438113,0.00002204194],"category_scores_gemma":[0.00004703604,0.0002251295,0.0001464636,0.0000972592,0.0004318133,0.00002118655,0.0001111091,0.00009495752,0.00000108339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002956869,"about_ca_system_score_gemma":0.0001055279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001156695,"about_ca_topic_score_gemma":0.00006030308,"domain_scores_codex":[0.9985455,0.0001160775,0.0003341612,0.0004907698,0.00007079655,0.0004427499],"domain_scores_gemma":[0.9993672,0.000009017082,0.0001343906,0.0003368178,0.00011247,0.00004013465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002033708,0.00005245694,0.009069637,0.00002557491,0.0001113161,0.000002788386,0.0002662138,0.0002222293,0.9858089,0.0001470764,0.003204965,0.0008854353],"study_design_scores_gemma":[0.002889008,0.002007809,0.004367379,0.0001739607,0.00009016356,0.00003249825,0.006398076,0.003421033,0.9621714,0.003015021,0.01450542,0.0009282139],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.929959,0.004647545,0.06349597,0.00001435293,0.00126413,0.0002513133,0.00002847823,0.00002821077,0.00031098],"genre_scores_gemma":[0.9815271,0.0001909545,0.01736943,0.00009360507,0.0005723429,0.00003220002,0.00008488159,0.00003525035,0.00009422103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0515681,"threshold_uncertainty_score":0.9180514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278874392716572,"score_gpt":0.3210499648148442,"score_spread":0.2982612208876784,"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."}}