{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003335579,0.001511727,0.001190965,0.002636069,0.001162383,0.002829322,0.002818292,0.002485023,0.5705411],"category_scores_gemma":[0.0201563,0.001622942,0.0009632793,0.002327524,0.0008946933,0.002965363,0.004586608,0.005818884,0.4545881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001592633,"about_ca_system_score_gemma":0.007065762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005195742,"about_ca_topic_score_gemma":0.01586689,"domain_scores_codex":[0.9980405,0.0004894681,0.0001634162,0.0001791923,0.0008572192,0.0002701849],"domain_scores_gemma":[0.9801314,0.004834088,0.0004597493,0.001017434,0.007875647,0.005681606],"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":[0.00001544801,0.00005244264,0.0000593993,0.0000618614,0.000001427351,0.00004735155,0.00003326892,0.0001422844,0.0002608084,0.0004311351,0.9394379,0.05945668],"study_design_scores_gemma":[0.00003586991,0.00005064183,0.0005834231,0.0001957692,0.000002873488,0.0002370507,0.0001084288,0.0003587509,0.0002691154,0.004801257,0.9933344,0.00002229394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.002596092,0.009978162,0.1489021,0.1054772,0.01756116,0.006998705,0.04723868,0.07875203,0.5824959],"genre_scores_gemma":[0.004483786,0.005170058,0.1507877,0.04002215,0.002689375,0.005439447,0.02018859,0.0144372,0.7567816],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5705411,"threshold_uncertainty_score":0.6125709,"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."}}