{"id":"W2546705495","doi":"","title":"WaterlooClarke: TREC 2015 Clinical Decision Support Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval; Mean reciprocal rank; Search engine; Reciprocal; Clinical decision support system; Test (biology); Rank (graph theory); Decision support system; Data mining","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":[0.01588101,0.002689178,0.00239111,0.007491503,0.002304787,0.004924522,0.003775383,0.003249478,0.03233977],"category_scores_gemma":[0.03375185,0.0009227378,0.001247411,0.005722338,0.001085069,0.004576976,0.002217749,0.002943837,0.01992835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009992594,"about_ca_system_score_gemma":0.01698072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1644995,"about_ca_topic_score_gemma":0.270072,"domain_scores_codex":[0.9933714,0.002027831,0.000941037,0.0008191824,0.002371358,0.0004691753],"domain_scores_gemma":[0.9685702,0.009968861,0.001862135,0.002199364,0.01474278,0.002656629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003070965,0.0002105154,0.0009338949,0.0009810907,0.0001031484,0.00007845258,0.00005550222,0.0008860623,0.001319415,0.0003740012,0.9654694,0.0292815],"study_design_scores_gemma":[0.003198814,0.001060146,0.02635803,0.001554394,0.0005518914,0.0007390126,0.0005435813,0.04867889,0.01784947,0.006426466,0.8926049,0.0004343838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03641379,0.02969932,0.02937449,0.05483222,0.00578544,0.008391572,0.737739,0.03154593,0.0662182],"genre_scores_gemma":[0.03816479,0.004605433,0.04460334,0.004586429,0.0008665811,0.00212677,0.8756398,0.001120256,0.02828665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1644995,"threshold_uncertainty_score":0.3270841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08993383767107038,"score_gpt":0.3806690461193441,"score_spread":0.2907352084482737,"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."}}