{"id":"W2373380784","doi":"","title":"York University at CLEF eHealth 2015: Medical Document Retrieval","year":2015,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Clef; Information retrieval; Computer science; eHealth; Relevance (law); Task (project management); World Wide Web; Normalization (sociology); Document retrieval; Query expansion; Information access; Health care","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":[0.01796097,0.002976303,0.00318594,0.004852243,0.002586478,0.003230621,0.002037808,0.002642401,0.03155423],"category_scores_gemma":[0.02772917,0.0007025881,0.001214689,0.003835048,0.001032454,0.003228776,0.002902918,0.002262888,0.01842056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003701289,"about_ca_system_score_gemma":0.004472665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02750907,"about_ca_topic_score_gemma":0.03185425,"domain_scores_codex":[0.9859871,0.005260935,0.001356774,0.002126926,0.004217368,0.001050852],"domain_scores_gemma":[0.9804524,0.005917436,0.000719249,0.003371292,0.006936222,0.00260341],"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":[0.003039684,0.001702813,0.004845733,0.001868151,0.0005164059,0.0002763876,0.0001804557,0.003464315,0.01523989,0.0008294236,0.7541952,0.2138416],"study_design_scores_gemma":[0.009957935,0.008128538,0.1162132,0.0008911001,0.0007979996,0.00271421,0.0008053413,0.1144137,0.1389488,0.00435082,0.6016617,0.001116767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3734373,0.07888238,0.07560829,0.02569433,0.01284254,0.01048765,0.2863871,0.05926996,0.07739045],"genre_scores_gemma":[0.2760959,0.005000432,0.08600713,0.003312498,0.001906806,0.003636357,0.5642831,0.002114757,0.05764307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03155423,"threshold_uncertainty_score":0.1055594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03629037255503566,"score_gpt":0.2925375850030408,"score_spread":0.2562472124480052,"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."}}