{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003703465,0.0007210331,0.0008403118,0.002493502,0.0009408345,0.002758007,0.001098959,0.0009730583,0.004886208],"category_scores_gemma":[0.01353924,0.000485249,0.0007058655,0.002894743,0.0005595122,0.001195449,0.001183564,0.00164354,0.002683173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005152411,"about_ca_system_score_gemma":0.0009563959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009498965,"about_ca_topic_score_gemma":0.001089205,"domain_scores_codex":[0.9975486,0.001448416,0.0001723683,0.0002616686,0.0004995101,0.00006954413],"domain_scores_gemma":[0.9916705,0.005651634,0.0006168062,0.0007430693,0.001042005,0.0002760502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000629871,0.0006314558,0.02378315,0.0010378,0.0003657532,0.000685819,0.0005155478,0.02314137,0.07410762,0.06238162,0.01891984,0.7938002],"study_design_scores_gemma":[0.0001316552,0.0003032128,0.01697042,0.0003076252,0.000314754,0.002765861,0.0003814103,0.478365,0.05389784,0.350182,0.09623797,0.0001422514],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03585221,0.004085971,0.9377461,0.004497265,0.000514856,0.0001538161,0.00132633,0.007609801,0.00821352],"genre_scores_gemma":[0.2209332,0.002334862,0.7670591,0.002541729,0.0006502375,0.0001686287,0.00213107,0.0008280505,0.003353082],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004886208,"threshold_uncertainty_score":0.01958603,"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."}}