{"id":"W2966871904","doi":"","title":"University of Waterloo Docker Images for OSIRRC at SIGIR 2019.","year":2019,"lang":"en","type":"article","venue":"International ACM SIGIR Conference on Research and Development in Information Retrieval","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007941608,0.00009852243,0.00012346,0.0005178876,0.0001094913,0.0001458964,0.0008850322,0.00007589978,0.0001883038],"category_scores_gemma":[0.0002367961,0.00009635396,0.00002294842,0.0002371673,0.00007941747,0.001331204,0.0005885015,0.0001752331,0.0002597842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003783029,"about_ca_system_score_gemma":0.0003393047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006960829,"about_ca_topic_score_gemma":0.00001906654,"domain_scores_codex":[0.9983981,0.0000395114,0.0002995689,0.0002147294,0.0008030778,0.0002450388],"domain_scores_gemma":[0.9982619,0.0004018965,0.0001235648,0.0002499143,0.0008919132,0.00007079633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01316136,0.0007657258,0.1149808,0.0009747362,0.0004501599,0.00002600864,0.03390671,0.001136897,0.02480256,0.2906839,0.05787138,0.4612397],"study_design_scores_gemma":[0.01137139,0.002379728,0.1852605,0.0007160233,0.000004159801,0.00003176432,0.002343912,0.0580682,0.28789,0.03275065,0.4178976,0.001286056],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9262549,0.00002169701,0.05362213,0.003990897,0.001109718,0.001193247,0.0000660844,0.00007986328,0.01366148],"genre_scores_gemma":[0.9802588,0.0001180268,0.01606643,0.000076571,0.00001763531,0.000007544958,0.00006679326,0.000003988882,0.003384164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4599537,"threshold_uncertainty_score":0.3929201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05017968987095447,"score_gpt":0.3071664114608667,"score_spread":0.2569867215899122,"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."}}