{"id":"W2989049020","doi":"10.1145/3357384.3358128","title":"Health Card Retrieval for Consumer Health Search","year":2019,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Automation, Chinese Academy of Sciences; University of California, San Diego; Wuhan University; Georgetown University; Technische Universität Berlin; Shanghai Jiao Tong University; Zhejiang University; Electronics and Telecommunications Research Institute; RMIT University; Tsinghua University; York University; Harbin University of Science and Technology; Harbin Institute of Technology; University of Illinois at Urbana-Champaign; Northeast Forestry University; Chinese Academy of Sciences; Tencent; Technische Universiteit Delft; Case Western Reserve University; Nanjing University; Microsoft Research; Lembaga Pengelola Dana Pendidikan; Università degli Studi di Udine; National University of Defense Technology; Arizona State University; University of North Carolina at Chapel Hill; Nanjing University of Aeronautics and Astronautics; Pennsylvania State University; Microsoft Research Asia; Peking University","keywords":"Computer science; Information retrieval; World Wide Web; Internet privacy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004113319,0.0008635087,0.001026446,0.009065474,0.0008312335,0.002645005,0.001223751,0.001473424,0.008711615],"category_scores_gemma":[0.02015827,0.0002252389,0.0008567685,0.005166768,0.0005655616,0.005171863,0.00116198,0.0009730028,0.004394093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408917,"about_ca_system_score_gemma":0.00147768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005957809,"about_ca_topic_score_gemma":0.009623799,"domain_scores_codex":[0.9960432,0.001680584,0.0004381277,0.0004895879,0.001163997,0.0001845629],"domain_scores_gemma":[0.9910767,0.005612716,0.0006019108,0.001114442,0.001382592,0.0002116695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000460809,0.0005586736,0.01357596,0.001273783,0.0001851978,0.000157945,0.0005590051,0.01185784,0.006444687,0.01901951,0.04081067,0.9050959],"study_design_scores_gemma":[0.0002947157,0.001065315,0.03255225,0.0004325205,0.0003522504,0.002043793,0.001962614,0.7533515,0.02232181,0.0576658,0.1276655,0.0002918407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1647272,0.01381945,0.7361897,0.004783556,0.0005511975,0.003162769,0.01423354,0.0117139,0.05081883],"genre_scores_gemma":[0.3903539,0.001996911,0.5846472,0.0006020253,0.0003712037,0.0004111475,0.01123807,0.000277409,0.01010218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009065474,"threshold_uncertainty_score":0.02914321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03126670483852918,"score_gpt":0.3521144026230345,"score_spread":0.3208476977845053,"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."}}