{"id":"W2139249401","doi":"10.1093/bioinformatics/btt673","title":"The International Society of Computational Biology presents: the Great Lakes Bioinformatics Conference, May 16–18, 2014, Cincinnati, Ohio","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data science; Computational biology; Bioinformatics; Library science; Biology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007998594,0.0002776273,0.0002455275,0.0000742325,0.0003632823,0.0002509855,0.001365623,0.0002817378,0.0002338114],"category_scores_gemma":[0.0003602727,0.0001550479,0.0002460849,0.0001660607,0.001153403,0.00004866897,0.0006699903,0.0002576245,0.0002232311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004724715,"about_ca_system_score_gemma":0.0003816251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003808437,"about_ca_topic_score_gemma":0.00003253242,"domain_scores_codex":[0.9974409,0.00005121607,0.00110068,0.0001594696,0.0007147754,0.0005329904],"domain_scores_gemma":[0.9975647,0.0002212211,0.0004960138,0.0006030326,0.0009430786,0.0001719148],"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":[0.00005054032,0.00009706835,0.001887028,0.0002362345,0.0004888874,1.420739e-7,0.0009709166,0.0002040408,0.001907149,0.001878603,0.9210998,0.07117958],"study_design_scores_gemma":[0.001655097,0.0005756903,0.00244841,0.00005790945,0.00004472045,0.00003152102,0.005045533,0.2685303,0.00819594,0.003754471,0.7091303,0.0005300969],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3313988,0.002786238,0.3057826,0.05085901,0.006814864,0.009049064,0.002388411,0.0002747706,0.2906461],"genre_scores_gemma":[0.8613816,0.01229407,0.09685405,0.006803271,0.001309817,0.0003302295,0.004500011,0.00009098524,0.0164359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5299828,"threshold_uncertainty_score":0.6322671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553924280974949,"score_gpt":0.2896759633183911,"score_spread":0.2641367205086416,"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."}}