{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007994296,0.00102818,0.001885528,0.00326938,0.001463764,0.001637599,0.00272525,0.002085187,0.00266951],"category_scores_gemma":[0.02848031,0.0008129757,0.001284968,0.00262641,0.001442806,0.004363323,0.001423861,0.002763892,0.001333967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002044084,"about_ca_system_score_gemma":0.002300212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005795665,"about_ca_topic_score_gemma":0.00855901,"domain_scores_codex":[0.9927753,0.003764963,0.0003237908,0.0008311548,0.002038338,0.0002664675],"domain_scores_gemma":[0.9847347,0.01085804,0.0007990042,0.0010123,0.002300104,0.0002959419],"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":[0.0004457899,0.0007054218,0.004260858,0.0004451953,0.0003154245,0.0001662274,0.0005262797,0.3246345,0.01002815,0.06143587,0.008772257,0.588264],"study_design_scores_gemma":[0.00009901643,0.0002330585,0.000877955,0.0000389253,0.000077864,0.0001417921,0.00004104874,0.9351597,0.002760403,0.05697856,0.00350022,0.00009141389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005008432,0.0004788614,0.9924157,0.0004480417,0.00003232149,0.00009103634,0.00005723822,0.0002998914,0.001168465],"genre_scores_gemma":[0.3329645,0.0009717467,0.6591423,0.0007913188,0.0005320723,0.000586424,0.0004341767,0.0001472747,0.004430134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007994296,"threshold_uncertainty_score":0.04227835,"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."}}