{"id":"W2616194201","doi":"10.1002/humu.23257","title":"CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants","year":2017,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; National Institutes of Health; Hospital for Sick Children","keywords":"Biology; Sanger sequencing; Prioritization; Computational biology; Genome; Genetics; Disease; Gene; Whole genome sequencing; Phenotype; Genomics; DNA sequencing; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002728371,0.0001035395,0.0001195017,0.00002358762,0.0004712213,0.0001015149,0.0002155943,0.0001066004,0.00001881474],"category_scores_gemma":[0.0001117946,0.0001045327,0.0001496805,0.000007829166,0.00007663944,0.000006821718,0.00008150558,0.00004202769,0.00001360378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007548485,"about_ca_system_score_gemma":0.00005446968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009404268,"about_ca_topic_score_gemma":0.00007402119,"domain_scores_codex":[0.9991761,0.00003186773,0.0002516515,0.000314152,0.0000654058,0.0001608527],"domain_scores_gemma":[0.9991886,0.000008968482,0.000198278,0.0004198682,0.0001125142,0.00007178777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000522805,0.001010997,0.008003626,0.0003292857,0.0005053155,0.0001349583,0.0009874464,0.0001246382,0.7429686,0.005625296,0.008541849,0.2312452],"study_design_scores_gemma":[0.01473903,0.002324292,0.8127457,0.0001343291,0.0007584967,0.0001018158,0.000895476,0.004699262,0.01865438,0.04199866,0.1006905,0.002257983],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900557,0.001183789,0.007269457,0.0001997411,0.000368479,0.0003149019,0.00007422709,0.00001193044,0.000521772],"genre_scores_gemma":[0.9973131,0.0002012987,0.0004677687,0.0001104,0.0006900796,0.00003908638,0.000553446,0.00002350629,0.000601327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8047422,"threshold_uncertainty_score":0.426272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08097337414239415,"score_gpt":0.3953557982809747,"score_spread":0.3143824241385805,"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."}}