{"id":"W1978851402","doi":"10.3389/fgene.2014.00325","title":"DaVIE: Database for the Visualization and Integration of Epigenetic data","year":2014,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Child and Family Research Institute","funders":"Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Visualization; Computer science; Database; Epigenetics; Data visualization; World Wide Web; Biology; Data mining; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008765224,0.00007999766,0.00009727378,0.00002861359,0.00007007919,0.00001094982,0.0002783336,0.00003228621,0.00002498595],"category_scores_gemma":[0.0002462492,0.00006612575,0.000007983456,0.0001076787,0.000187868,0.00009229746,0.0002382539,0.00005819891,0.000003212693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004179834,"about_ca_system_score_gemma":0.000007158409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006880263,"about_ca_topic_score_gemma":0.0001362847,"domain_scores_codex":[0.9990684,0.0001047213,0.0002090933,0.0003161327,0.0001510786,0.0001506235],"domain_scores_gemma":[0.9991891,0.0001231621,0.00008721171,0.0005567489,0.000004569669,0.00003924816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004010402,0.0001135863,0.2717608,0.00006486101,0.00001439953,3.061458e-7,0.0010791,0.0027491,0.01264157,0.0001649186,0.006721944,0.7046494],"study_design_scores_gemma":[0.0005272954,0.0001420815,0.1720222,0.00003056873,0.00004246333,7.224915e-7,0.000386723,0.7957584,0.00643719,0.001083488,0.02342077,0.0001480471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08607068,0.0005240497,0.9123902,0.0001114959,0.000172752,0.0005124988,0.00005106878,0.000004384477,0.0001628855],"genre_scores_gemma":[0.9271438,0.001824486,0.07049128,0.0002556119,0.00003781196,0.00002783421,0.0001620603,0.00001666448,0.00004046115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8418989,"threshold_uncertainty_score":0.269653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03316461524196006,"score_gpt":0.2996000274020513,"score_spread":0.2664354121600913,"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."}}