{"id":"W4249868975","doi":"10.1158/1538-7445.am2016-130","title":"Abstract 130: International Cancer Genome Consortium (ICGC)","year":2016,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Genome; Genomics; Cancer; Globe; Biology; Computational biology; Library science; Computer science; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0180579,0.001629047,0.002161272,0.009063016,0.001921251,0.007564884,0.004869298,0.002632919,0.09353058],"category_scores_gemma":[0.04480246,0.001075299,0.001170746,0.02389717,0.0009436135,0.002223275,0.006050791,0.003455846,0.04932961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006710443,"about_ca_system_score_gemma":0.03454255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1222578,"about_ca_topic_score_gemma":0.08613263,"domain_scores_codex":[0.9883278,0.002153575,0.001078863,0.002077499,0.004879566,0.001482781],"domain_scores_gemma":[0.9500145,0.005028833,0.002026709,0.008403707,0.02682474,0.007701492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008608987,0.00001483183,0.0007877924,0.000222003,0.00002390785,0.00001671673,0.00003694264,0.0001151941,0.0001007782,0.001131087,0.9878269,0.009637776],"study_design_scores_gemma":[0.0001160676,0.00002250414,0.007994815,0.0003337123,0.00003477077,0.00003582524,0.00004620995,0.0001452196,0.0002686099,0.0007082965,0.9902609,0.00003318357],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.000666403,0.0009852855,0.001238267,0.005747013,0.001946184,0.0006985971,0.9588131,0.001795216,0.0281099],"genre_scores_gemma":[0.001992698,0.0006234395,0.00227774,0.001491088,0.0003256376,0.001138461,0.9809508,0.0007779606,0.01042205],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1222578,"threshold_uncertainty_score":0.3128909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05659074611804318,"score_gpt":0.3914149615093285,"score_spread":0.3348242153912854,"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."}}