{"id":"W2100044360","doi":"10.1371/journal.pcbi.1003510","title":"A Quick Guide to Genomics and Bioinformatics Training for Clinical and Public Audiences","year":2014,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Institute of Biomedical Imaging and Bioengineering; Staatssekretariat für Bildung, Forschung und Innovation; Directorate for Biological Sciences; Swiss Institute of Bioinformatics; Government of Ontario; Biotechnology and Biological Sciences Research Council; Ontario Institute for Cancer Research","keywords":"Genomics; Training (meteorology); Computer science; Computational biology; Bioinformatics; Data science; Biology; Genetics; Genome; Gene; Geography","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":[0.0007047174,0.0001205431,0.000205486,0.00008385759,0.000125319,0.0000472526,0.0001645043,0.0001689795,0.000003321846],"category_scores_gemma":[0.00168641,0.0001007905,0.0000449023,0.00005591358,0.0004627898,0.000005517289,0.0002146402,0.0000705595,0.000008049805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006512056,"about_ca_system_score_gemma":0.0001440952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000242117,"about_ca_topic_score_gemma":0.00001475162,"domain_scores_codex":[0.9987918,0.00006299824,0.0004714577,0.0002509326,0.0001047515,0.0003180225],"domain_scores_gemma":[0.9990356,0.0002931653,0.0000875583,0.0001130931,0.0001924975,0.0002780719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003197996,0.0002564629,0.04237416,0.000384043,0.0004468524,5.61596e-7,0.001437455,0.0002002984,0.01518706,0.01308487,0.01390317,0.9124053],"study_design_scores_gemma":[0.003690851,0.006873346,0.02514663,0.00004303806,0.00005301462,0.00005418567,0.001491011,0.2977679,0.001287225,0.01610343,0.6465896,0.0008997816],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7762215,0.0002156947,0.2193661,0.00287104,0.0001644611,0.000423611,0.00007857927,0.00001532979,0.0006436632],"genre_scores_gemma":[0.7645031,0.0004254444,0.2315544,0.002676452,0.0003963737,0.00003450953,0.0003306058,0.00001155645,0.0000675129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9115055,"threshold_uncertainty_score":0.4110118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08156143338425587,"score_gpt":0.371029936679115,"score_spread":0.2894685032948591,"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."}}