{"id":"W2395907205","doi":"10.1186/s40246-016-0063-5","title":"Human genome meeting 2016","year":2016,"lang":"en","type":"article","venue":"Human Genomics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Université de Sherbrooke; McGill University; University of British Columbia","funders":"Nestec; Danone","keywords":"Human genetics; Genome Biology; Human genome; Biology; Computational biology; Genome; Genetics; Personal genomics; Genomics; Gene","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":[0.0001741803,0.0001921718,0.0001569344,0.00004877092,0.0002406337,0.00004164097,0.0003145416,0.0001347457,0.0001299434],"category_scores_gemma":[0.00003413506,0.0001603431,0.0001005824,0.00003099536,0.0001082583,0.000003138927,0.0002443378,0.00005332821,0.0001405056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008097901,"about_ca_system_score_gemma":0.00008514765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003669955,"about_ca_topic_score_gemma":0.000117751,"domain_scores_codex":[0.9988236,0.00002380649,0.000281978,0.0004221034,0.00008390682,0.0003645406],"domain_scores_gemma":[0.9991247,0.00001277712,0.0001196059,0.0005427305,0.00007118414,0.0001289777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001018052,0.0000280592,0.0006120638,0.000005832751,0.00003366534,0.000003180313,0.00003208476,0.00002223057,0.9922892,0.001266122,0.004602834,0.001094518],"study_design_scores_gemma":[0.001552156,0.0004136906,0.0105656,0.00003155017,0.00003972831,0.00001650669,0.00005022653,0.000002553683,0.1648396,0.001975948,0.8198032,0.0007092562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863389,0.0008623205,0.0005512889,0.0001282733,0.0001705886,0.0001613289,0.00009355666,0.00002376934,0.01166999],"genre_scores_gemma":[0.9919818,0.0004022903,0.0004417711,0.0004158853,0.0012324,0.00001987869,0.0001009232,0.00006356447,0.005341457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8274497,"threshold_uncertainty_score":0.6538602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282574438570427,"score_gpt":0.2470131399698772,"score_spread":0.2341873955841729,"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."}}