{"id":"W2908509829","doi":"","title":"Reconstructing evolutionary trajectories of mutations in cancer","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mutation; Genetics; Computational biology; Evolutionary biology; Biology; 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":[0.0000601903,0.00006309609,0.00006601342,0.0001030876,0.00006520726,0.00001776893,0.0001194983,0.00004280615,0.0003200771],"category_scores_gemma":[0.0004336143,0.00007207313,0.00003436786,0.00009748541,0.0001458948,0.000007138352,0.00003901101,0.00008706548,0.000007722614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003113628,"about_ca_system_score_gemma":0.0001642289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004280233,"about_ca_topic_score_gemma":0.0003867489,"domain_scores_codex":[0.9993877,0.00003241348,0.0001913474,0.0001913712,0.000109724,0.00008737868],"domain_scores_gemma":[0.9993924,0.0000533825,0.0001053747,0.0001013315,0.0003232941,0.00002425488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000167166,0.0001374962,0.7712505,0.000008276925,0.0001024434,0.000002586813,0.001522212,0.009699026,0.1783588,0.02870292,0.0007880281,0.009260526],"study_design_scores_gemma":[0.003197238,0.001416523,0.6928153,0.0004072567,0.00005907044,0.00004614219,0.009969965,0.05268201,0.2016553,0.01329665,0.02352489,0.0009297067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765862,0.00004458749,0.0005026603,0.0005600625,0.0004715098,0.00007660226,0.00004893887,0.000007226739,0.02170221],"genre_scores_gemma":[0.997942,0.0001481683,0.0006985365,0.00003851932,0.0002682953,0.00003565989,0.0001193626,0.000007630834,0.0007418354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07843522,"threshold_uncertainty_score":0.350462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03641358159742272,"score_gpt":0.3518077609820487,"score_spread":0.315394179384626,"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."}}