{"id":"W2025564971","doi":"10.1038/gim.2014.178","title":"A high-resolution copy-number variation resource for clinical and population genetics","year":2014,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Mount Sinai Hospital; Lunenfeld-Tanenbaum Research Institute; University Health Network; University of Toronto; SickKids Foundation; Public Health Ontario; Hospital for Sick Children","funders":"National Cancer Institute; Hospital for Sick Children; York University; Government of Ontario; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Genomics Institute; University of Toronto; GlaxoSmithKline","keywords":"Copy-number variation; Genotyping; Genetics; Biology; Population; Population genomics; Copy number analysis; Medical genetics; Genotype; Genomics; Computational biology; Medicine; Gene; Genome","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003782033,0.0007256973,0.0008351257,0.003289086,0.0007808581,0.00135181,0.002256318,0.0007051905,0.01326542],"category_scores_gemma":[0.01120817,0.0004668391,0.0004510272,0.003957361,0.0005283006,0.00075666,0.001606701,0.0006632759,0.00575371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003188232,"about_ca_system_score_gemma":0.005902134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05200639,"about_ca_topic_score_gemma":0.08726244,"domain_scores_codex":[0.9969997,0.0004206048,0.0001955884,0.0007147813,0.00152779,0.0001414382],"domain_scores_gemma":[0.9908525,0.002495329,0.00120879,0.002138029,0.002667227,0.0006380908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001635524,0.0002373027,0.157345,0.001443861,0.0005624113,0.001141809,0.001217143,0.01119153,0.1801151,0.006122447,0.1416834,0.4973045],"study_design_scores_gemma":[0.0005796753,0.0003461984,0.4639383,0.0003295694,0.0005339577,0.002597341,0.0002788874,0.04824783,0.09714637,0.009554972,0.3761804,0.0002665389],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1779315,0.003347639,0.4042597,0.002359823,0.0001826504,0.001885225,0.3501875,0.0369405,0.02290541],"genre_scores_gemma":[0.2370965,0.000958442,0.4535574,0.0005552912,0.0001906881,0.001699719,0.2915431,0.002741425,0.01165739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05200639,"threshold_uncertainty_score":0.1034074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02238157548614884,"score_gpt":0.3119189704424863,"score_spread":0.2895373949563375,"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."}}