{"id":"W2060816264","doi":"10.1145/1571941.1571995","title":"A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval","year":2009,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Ranking (information retrieval); Computer science; Bayesian probability; Artificial intelligence; Biomedicine; Learning to rank; Machine learning; Domain (mathematical analysis); Information retrieval; Bayesian inference; Mathematics; 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":[0.001080183,0.00008070714,0.0001103985,0.0003477609,0.0003052652,0.0001939361,0.0004489345,0.00006410682,0.000004722667],"category_scores_gemma":[0.000250654,0.00007062966,0.00004330418,0.0008729079,0.00001132392,0.001840704,0.0002565903,0.0001686019,0.00001846327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008020515,"about_ca_system_score_gemma":0.00005032499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001273669,"about_ca_topic_score_gemma":4.586656e-7,"domain_scores_codex":[0.9988046,0.00003199366,0.0002830684,0.0001322914,0.0004402437,0.0003078498],"domain_scores_gemma":[0.9995488,0.00003582087,0.00005880712,0.0001170749,0.000109927,0.0001295701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002724684,0.0004181645,0.004505234,0.0001435524,0.00001022244,0.000004664786,0.08598507,0.001754397,0.0007748854,0.1094901,0.0006626667,0.7959785],"study_design_scores_gemma":[0.00115955,0.0004667993,0.01034168,0.00002572567,0.000002471922,0.000008142272,0.0003511666,0.9831022,0.0007148873,0.0008114285,0.002791773,0.000224147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04096011,8.835385e-7,0.9527091,0.000982244,0.00005310341,0.0005207351,7.406696e-7,0.0001339114,0.004639123],"genre_scores_gemma":[0.9187841,3.699304e-7,0.08053049,0.0005856578,0.0000194472,0.000004475717,0.00001454742,0.000001251346,0.00005965021],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9813479,"threshold_uncertainty_score":0.2880194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502986172271912,"score_gpt":0.2657594338881005,"score_spread":0.2407295721653814,"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."}}