{"id":"W4293105950","doi":"10.1002/ggn2.202200016","title":"GA4GH Phenopackets: A Practical Introduction","year":2022,"lang":"en","type":"article","venue":"Advanced Genetics","topic":"Ocular Oncology and Treatments","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Human Genome Research Institute; National Institutes of Health; Alan Turing Institute","keywords":"Suite; Schema (genetic algorithms); Disease; Medical diagnosis; Genomics; Computer science; Data science; Health care; Data sharing; Alliance; Clinical phenotype; Computational biology; Bioinformatics; Medicine; Phenotype; Genome; Biology; Information retrieval; Genetics; Pathology; Alternative medicine; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.00007235586,0.00007429039,0.0001200928,0.00003553349,0.0001499435,0.000002519516,0.00002815724,0.00002828327,0.0005727959],"category_scores_gemma":[0.0000648805,0.00007354745,0.00003522508,0.0001256257,0.00003937907,0.00002880851,0.00005464453,0.0001976571,0.00006446526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000120414,"about_ca_system_score_gemma":0.00007837995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.928733e-7,"about_ca_topic_score_gemma":9.914302e-7,"domain_scores_codex":[0.9993212,0.00005473664,0.0001080165,0.0002004,0.0001638513,0.0001518247],"domain_scores_gemma":[0.9995992,0.00002538889,0.00004360349,0.0002336563,0.0000351129,0.00006301849],"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":[0.007340348,0.01055074,0.1089344,0.0001714251,0.001678291,0.003995337,0.001906046,0.009184802,0.1424301,0.01148751,0.05085447,0.6514665],"study_design_scores_gemma":[0.004157517,0.003300131,0.01000889,0.0000038396,0.0003396607,0.001395085,0.0005000365,0.0002858893,0.01402045,0.002660991,0.9631505,0.0001770256],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773138,0.001570063,0.001104902,0.0153612,0.0009927717,0.0004805891,0.00001024915,0.00009170922,0.003074735],"genre_scores_gemma":[0.9622641,0.0001405527,0.03362583,0.001150601,0.0003558539,0.00008054406,0.00008081832,0.0000205961,0.002281136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.912296,"threshold_uncertainty_score":0.6271713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636901947465111,"score_gpt":0.333818773909665,"score_spread":0.3174497544350139,"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."}}