{"id":"W2742478344","doi":"10.1093/bib/bbx100","title":"A global perspective on evolving bioinformatics and data science training needs","year":2017,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":177,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Natural Environment Research Council; Medical Research Council; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"Globe; Stewardship (theology); Trainer; Training (meteorology); Curriculum; Perspective (graphical); Appeal; Engineering ethics; Principal (computer security); Interpretation (philosophy); Personalization; Computer science; Data science; Political science; Medicine; Psychology; Artificial intelligence; World Wide Web; Engineering; Pedagogy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001705844,0.0002983345,0.0002975338,0.000291549,0.0009217456,0.0008762408,0.002279375,0.0002267605,0.000007287323],"category_scores_gemma":[0.004821381,0.0002669824,0.00005136316,0.0003296876,0.001876324,0.0002198258,0.002378739,0.0002481446,0.00002475939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164361,"about_ca_system_score_gemma":0.0006697819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002418854,"about_ca_topic_score_gemma":0.0001500606,"domain_scores_codex":[0.9973242,0.00001717051,0.0006777058,0.0003489055,0.0008071571,0.0008248994],"domain_scores_gemma":[0.9970905,0.00004001461,0.0003801566,0.001845858,0.0002937114,0.0003497452],"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":[0.0007395872,0.0007247213,0.03499687,0.001769255,0.0004082152,0.0000579983,0.04797133,0.0002166692,0.004934416,0.03645831,0.03570608,0.8360165],"study_design_scores_gemma":[0.009205602,0.003036344,0.1401437,0.001327606,0.0001090532,0.0005921639,0.07954668,0.6799302,0.004917242,0.004299591,0.07355378,0.003338098],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6770812,0.0008797567,0.01672185,0.008104244,0.001441622,0.001990307,0.000880603,0.0001306062,0.2927698],"genre_scores_gemma":[0.9556684,0.0008339148,0.04130439,0.001740177,0.0001814463,0.000009303093,0.0001104246,0.00001977086,0.0001321366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8326784,"threshold_uncertainty_score":0.9999782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05193494988416302,"score_gpt":0.352883202144799,"score_spread":0.3009482522606359,"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."}}