{"id":"W2947689917","doi":"","title":"KlickLabs at TREC 2018 Precision Medicine track.","year":2018,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Track (disk drive); Precision medicine; Information retrieval; Artificial intelligence; 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":[0.009426671,0.004717301,0.003718633,0.01109041,0.003455893,0.008887012,0.00532603,0.003476197,0.1835662],"category_scores_gemma":[0.02271781,0.001075642,0.001986421,0.008555487,0.001077322,0.01119517,0.004880049,0.003768427,0.1829371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006463628,"about_ca_system_score_gemma":0.008451271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07185958,"about_ca_topic_score_gemma":0.1127988,"domain_scores_codex":[0.9941239,0.001200474,0.0004547167,0.001026046,0.002481566,0.0007132689],"domain_scores_gemma":[0.9804698,0.003541829,0.0007079813,0.002460223,0.01031441,0.00250581],"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":[0.00005638383,0.00004581788,0.00008601863,0.0001743187,0.00001608106,0.00001845155,0.00001116886,0.0001175551,0.0002978091,0.0002977946,0.9896768,0.009201858],"study_design_scores_gemma":[0.0004139721,0.0001540069,0.003030906,0.0003752885,0.0001276161,0.0001702516,0.0002420317,0.005810654,0.00442948,0.006621255,0.9785097,0.0001149404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.004207891,0.01071939,0.0137089,0.02440011,0.01175343,0.001047832,0.8263688,0.04214679,0.06564679],"genre_scores_gemma":[0.005906092,0.0021198,0.01493904,0.002180103,0.001210663,0.0005395524,0.9035081,0.002217618,0.06737916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1835662,"threshold_uncertainty_score":0.6140901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04302524158150629,"score_gpt":0.3146855815370476,"score_spread":0.2716603399555413,"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."}}