{"id":"W7116903820","doi":"10.1016/j.transci.2025.104300","title":"Bioinformatic applications in blood group molecular typing","year":2025,"lang":"en","type":"article","venue":"Transfusion and Apheresis Science","topic":"Blood groups and transfusion","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Blood Services","funders":"","keywords":"Typing; Blood group antigens; Blood typing; Software; Set (abstract data type); Software tool","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":[0.0002488236,0.00009282096,0.0001493978,0.0002686671,0.0001989026,0.00003396875,0.0001044013,0.00004635188,0.0000439613],"category_scores_gemma":[0.00001102657,0.00007372812,0.00003821407,0.001362467,0.0002260322,0.0001321853,0.00002137101,0.0001161118,0.000003492882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001178414,"about_ca_system_score_gemma":0.00007221998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001336346,"about_ca_topic_score_gemma":0.0000998601,"domain_scores_codex":[0.999179,0.000009712168,0.0001979305,0.0002480791,0.0001666881,0.0001985665],"domain_scores_gemma":[0.9996478,0.00002538283,0.00001518687,0.0001845635,0.00003243353,0.00009463196],"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":[0.00006543785,0.0005168416,0.04522915,0.0004242273,0.00001642579,0.00001091131,0.0006568968,0.000005859707,0.8391686,0.04178579,0.00001196646,0.0721079],"study_design_scores_gemma":[0.009557159,0.00041669,0.7667966,0.001668219,0.0004633892,0.00007017064,0.002893851,0.00375287,0.2054068,0.004422755,0.003944237,0.0006073422],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976253,0.0005409238,0.008998819,0.0007056572,0.00002945816,0.0004896587,0.000001332613,0.00004048221,0.01294072],"genre_scores_gemma":[0.9971033,0.0006875296,0.001644591,0.0004426636,0.000004588077,0.00002971419,0.000002068232,0.000003915086,0.00008164411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7215674,"threshold_uncertainty_score":0.3006545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006089798398837403,"score_gpt":0.2547855336463236,"score_spread":0.2486957352474862,"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."}}