{"id":"W4247194577","doi":"10.1158/1538-7445.am2017-377","title":"Abstract 377: International Cancer Genome Consortium (ICGC)","year":2017,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Genome; Genomics; Cancer; Globe; Library science; Computational biology; Biology; Genetics; Computer science; 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":[0.01620783,0.001529031,0.001981656,0.007668762,0.001924061,0.006607942,0.004731267,0.002787166,0.09330532],"category_scores_gemma":[0.03791086,0.0008783838,0.001023713,0.02018961,0.0009032488,0.001923612,0.004921793,0.003921121,0.04548676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006939768,"about_ca_system_score_gemma":0.04026766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1369733,"about_ca_topic_score_gemma":0.09165308,"domain_scores_codex":[0.9902581,0.001991565,0.0008102006,0.001706223,0.003868691,0.001365334],"domain_scores_gemma":[0.956991,0.003630298,0.001544013,0.00647004,0.02358452,0.007780091],"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":[0.00009757937,0.00001520476,0.0006602323,0.0002233637,0.00002276802,0.00001781693,0.00003632679,0.0001057123,0.0001134422,0.001516645,0.9857504,0.01144053],"study_design_scores_gemma":[0.0001162171,0.00002113783,0.005862917,0.0003030572,0.00003031845,0.00003241658,0.0000448326,0.0001085074,0.0002414646,0.0007627192,0.9924501,0.00002622973],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.000992839,0.002035448,0.001991148,0.01226291,0.00371039,0.001265213,0.9115085,0.002552555,0.06368089],"genre_scores_gemma":[0.003761328,0.001388804,0.004202539,0.003799684,0.000618631,0.002134238,0.9588219,0.00130484,0.02396812],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1369733,"threshold_uncertainty_score":0.3121374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08232470423160727,"score_gpt":0.4283289756921895,"score_spread":0.3460042714605822,"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."}}