{"id":"W2886441288","doi":"10.1158/1538-7445.am2018-3005","title":"Abstract 3005: International Cancer Genome Consortium","year":2018,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"","keywords":"Genome; Genomics; Globe; Cancer; Library science; Biology; Computational biology; Geography; 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":[0.01472692,0.001344642,0.001629834,0.01004911,0.002101019,0.00667714,0.005172322,0.002362353,0.1054938],"category_scores_gemma":[0.03454795,0.0008303105,0.001128601,0.02652018,0.0007149752,0.002301336,0.005471897,0.003532781,0.0494782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006756736,"about_ca_system_score_gemma":0.038374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1270351,"about_ca_topic_score_gemma":0.09308855,"domain_scores_codex":[0.9896182,0.002019267,0.0009323322,0.001768387,0.004324372,0.001337357],"domain_scores_gemma":[0.96812,0.002990393,0.001342492,0.004822406,0.01743633,0.005288463],"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":[0.00008048953,0.00001905038,0.0008131019,0.0001891146,0.00002296287,0.0000194978,0.0000427002,0.0001157391,0.00007398683,0.003365587,0.9806892,0.01456856],"study_design_scores_gemma":[0.00009021139,0.00001467091,0.004417217,0.0001958771,0.00002186007,0.00002855129,0.00005039624,0.0001287998,0.0001410551,0.00106475,0.9938238,0.00002276616],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0008677795,0.001371357,0.002495852,0.009447195,0.002235654,0.001280658,0.883101,0.00246278,0.09673777],"genre_scores_gemma":[0.002907655,0.00101965,0.004486125,0.002962396,0.0003915012,0.002399471,0.9566203,0.0009637629,0.0282491],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1270351,"threshold_uncertainty_score":0.3529118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05616339917084291,"score_gpt":0.4103613090822065,"score_spread":0.3541979099113636,"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."}}