{"id":"W2168495270","doi":"10.1186/gm308","title":"Looking back at genomic medicine in 2011","year":2012,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Center for Research Resources; National Institute of Neurological Disorders and Stroke","keywords":"Genomic medicine; Human genetics; Genome Biology; Computational biology; Computational genomics; Medicine; Genomics; Bioinformatics; Biology; Genetics; Genome; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000461745,0.0001867541,0.0002530304,0.000103632,0.00007421112,0.00000254532,0.000190918,0.0001117997,0.004123985],"category_scores_gemma":[0.00003397889,0.00014942,0.00003685402,0.00007906394,0.0001652744,0.000005621177,0.0001663228,0.00008768271,0.0004264283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008554542,"about_ca_system_score_gemma":0.00002733601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002269792,"about_ca_topic_score_gemma":0.00008649271,"domain_scores_codex":[0.9987426,0.00005371892,0.0003655221,0.0002474378,0.0001325937,0.0004580706],"domain_scores_gemma":[0.9993389,0.00001460367,0.00009775181,0.0003524912,0.00003638839,0.0001598785],"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":[0.00006475906,0.00005131244,0.1463991,0.00004727999,0.00005051169,0.000004509428,0.001976156,0.00004280316,0.8443459,0.0003813577,0.006179872,0.0004564983],"study_design_scores_gemma":[0.002826561,0.0005404145,0.4696658,0.00005592499,0.00005848349,0.0001690609,0.001091461,0.00001106307,0.009469954,0.0001446624,0.5155197,0.0004469168],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965427,0.01092782,0.0001845376,0.001161744,0.0004694529,0.0001746813,0.00000912744,0.00000906326,0.02163651],"genre_scores_gemma":[0.9876528,0.0005553103,0.0001832896,0.000756721,0.002122774,0.00001583914,0.0002476298,0.00002621534,0.008439403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8348759,"threshold_uncertainty_score":0.9967864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302896013052391,"score_gpt":0.2316940645139455,"score_spread":0.2186651043834216,"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."}}