{"id":"W4413692675","doi":"10.64628/ab.pfypfveda","title":"Artificial intelligence uses biggest disease database to fight cancer","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Cancer Research","funders":"","keywords":"Disease; Database; Computer science; Artificial intelligence; Medicine; Internal 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":[0.001715289,0.0007362233,0.0007517515,0.003250966,0.0005968713,0.002426358,0.001653371,0.0009181031,0.006981221],"category_scores_gemma":[0.006268794,0.0004717094,0.0008525532,0.002591727,0.0003421504,0.002229342,0.00177282,0.0009328075,0.002634736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005349375,"about_ca_system_score_gemma":0.001152024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003744832,"about_ca_topic_score_gemma":0.004116508,"domain_scores_codex":[0.998958,0.0001886882,0.0001114178,0.0002962251,0.000363129,0.00008256109],"domain_scores_gemma":[0.9977248,0.0005541937,0.0001142565,0.001079985,0.0003350193,0.0001916952],"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":[0.002917364,0.0004785716,0.03999208,0.001000057,0.0009628247,0.001039046,0.0003349725,0.02645534,0.01236467,0.0446538,0.4146622,0.4551391],"study_design_scores_gemma":[0.0008171494,0.0004632639,0.02032217,0.0003353663,0.0007843211,0.002194137,0.0003961473,0.3090183,0.04437834,0.1351267,0.4860244,0.0001397808],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.14859,0.009126644,0.3873733,0.01501097,0.002837395,0.0007162406,0.2245662,0.1251315,0.08664784],"genre_scores_gemma":[0.4653156,0.003134288,0.2806759,0.002428243,0.0004573332,0.0002921458,0.2316928,0.002977041,0.0130268],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.006981221,"threshold_uncertainty_score":0.02335453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06075711097654894,"score_gpt":0.3521194012029543,"score_spread":0.2913622902264054,"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."}}