{"id":"W3177257816","doi":"","title":"MRG_UWaterloo Participation in the TREC 2020 Precision Medicine Track.","year":2020,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; University of Waterloo","funders":"","keywords":"Track (disk drive); Computer science; Precision medicine; Artificial intelligence; Information retrieval; Medicine","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":[0.01175354,0.001582887,0.002066788,0.002966821,0.00415376,0.005982981,0.002665685,0.004052326,0.1611064],"category_scores_gemma":[0.009002735,0.0005190745,0.0009007745,0.002704711,0.001100254,0.002851284,0.003604298,0.002102124,0.100369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00810436,"about_ca_system_score_gemma":0.01874963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1997669,"about_ca_topic_score_gemma":0.4366699,"domain_scores_codex":[0.9930317,0.001323031,0.0001676267,0.000787299,0.003474806,0.001215533],"domain_scores_gemma":[0.9872224,0.0007900037,0.0002590883,0.0005717602,0.007226313,0.003930469],"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":[0.00006614546,0.00004178687,0.0001325768,0.0000522142,0.000006794677,0.00001246545,0.00001698602,0.00005358145,0.0004025596,0.0003319098,0.9890485,0.009834483],"study_design_scores_gemma":[0.0001039518,0.00009703416,0.001862663,0.00006115474,0.00001837891,0.00003010146,0.0001213478,0.0007042689,0.001276667,0.0008641321,0.9948364,0.0000239024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01062902,0.01496376,0.01179612,0.1268049,0.05397582,0.00357807,0.2288406,0.007762763,0.541649],"genre_scores_gemma":[0.01793395,0.002189801,0.008404245,0.01739811,0.005792409,0.0006782764,0.09822963,0.000884956,0.8484887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1997669,"threshold_uncertainty_score":0.5389545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0453331524621173,"score_gpt":0.3157117641576203,"score_spread":0.270378611695503,"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."}}