{"id":"W1986268410","doi":"10.1016/j.jbi.2014.04.015","title":"A Performance Weighted Collaborative Filtering algorithm for personalized radiology education","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Collaborative filtering; Computer science; Metric (unit); Machine learning; Artificial intelligence; Recommender system; Personalized medicine; Function (biology); Algorithm; Data mining; Bioinformatics","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":[0.0010446,0.00009863592,0.00028744,0.0002316389,0.00008192757,0.00008192489,0.000497407,0.00009474835,0.000005654264],"category_scores_gemma":[0.00006982413,0.00006983137,0.00007868512,0.0002784301,0.00006906509,0.0006761458,0.00005720704,0.0001387228,0.000002746229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006401297,"about_ca_system_score_gemma":0.0003015101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000010517,"about_ca_topic_score_gemma":8.177301e-8,"domain_scores_codex":[0.9986786,0.00005528487,0.0007815787,0.00005206444,0.0002619995,0.0001704187],"domain_scores_gemma":[0.998477,0.0001522524,0.0006743304,0.0001472477,0.00040632,0.0001428382],"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":[0.000007282994,0.00006656972,0.00002215232,0.0000923068,0.00003973563,4.325674e-7,0.002429139,6.064835e-7,0.0001094655,0.004359681,0.02354334,0.9693293],"study_design_scores_gemma":[0.0007546243,0.001040139,0.00005575797,0.0001542606,0.00001043159,0.0003854141,0.0003477343,0.4832879,0.0008330687,0.001104646,0.5119019,0.0001240647],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005115246,0.0000701673,0.9921844,0.000755158,0.001031476,0.0001542826,0.000002908551,0.00002896529,0.0006573952],"genre_scores_gemma":[0.02617419,0.0001083338,0.9725583,0.0006363241,0.0003993779,0.00001842499,0.000005501282,0.00000527978,0.00009424043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9692052,"threshold_uncertainty_score":0.2847641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093561554543765,"score_gpt":0.2692834530778626,"score_spread":0.2583478375324249,"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."}}