{"id":"W2365672368","doi":"","title":"An Investigation of the Eectiveness of Concept-based Approach in Medical Information Retrieval GRIUM @ CLEF2014eHealthTask 3","year":2014,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Clef; Computer science; Information retrieval; Task (project management); Unified Medical Language System; Natural language processing; Artificial intelligence; Resource (disambiguation); Domain (mathematical analysis)","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":[0.0006929658,0.00005775508,0.000115166,0.0000324703,0.00001891869,0.000002931957,0.0002001093,0.0002141548,0.000007724679],"category_scores_gemma":[0.0003973465,0.00003756521,0.00003124311,0.0001346248,0.0003573219,0.000004735065,0.00003200008,0.00008009726,2.550619e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006854566,"about_ca_system_score_gemma":0.0001831979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008818363,"about_ca_topic_score_gemma":0.0000194725,"domain_scores_codex":[0.999113,0.0002087137,0.000267289,0.00009603132,0.000222144,0.00009287505],"domain_scores_gemma":[0.9995005,0.00003326352,0.0001262413,0.0002090795,0.00007686861,0.00005400944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001654685,0.0008548744,0.4403698,0.001310098,0.00009078314,4.254638e-7,0.003428728,0.006019703,0.3402664,0.00786369,0.002621304,0.1955195],"study_design_scores_gemma":[0.001918733,0.0008913433,0.2824923,0.0000657175,0.000009618612,0.0000023192,0.0005391798,0.02974718,0.6824939,0.0003053983,0.001372693,0.0001616503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718646,0.00002737809,0.02728217,0.0003137296,0.00007339517,0.00009517209,0.000005558549,0.000006428371,0.0003315216],"genre_scores_gemma":[0.9980756,0.000001932233,0.001361234,0.000441057,0.00003196699,0.000003075843,0.00007794008,0.000002581227,0.000004606279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3422275,"threshold_uncertainty_score":0.1651758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01115093977129494,"score_gpt":0.2625488301593654,"score_spread":0.2513978903880704,"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."}}