{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008124778,0.0001233969,0.0001958351,0.00004136591,0.00007562275,0.00001093889,0.00009381981,0.0001930674,0.0000163621],"category_scores_gemma":[0.0002378633,0.0001175899,0.00003175363,0.00006118142,0.00009960429,0.000002274568,0.00005983116,0.00007312233,0.00000226506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001325627,"about_ca_system_score_gemma":0.00002145742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000800124,"about_ca_topic_score_gemma":0.00009719496,"domain_scores_codex":[0.9988058,0.0001303244,0.0004526852,0.0003131713,0.0001133047,0.0001847229],"domain_scores_gemma":[0.9994209,0.00006063562,0.0001189655,0.0002571768,0.00007562471,0.00006667701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002589431,0.0001173002,0.9481323,0.0001056019,0.00006921319,6.627868e-7,0.0004425507,0.003216922,0.01205768,0.008333492,0.005393847,0.02187148],"study_design_scores_gemma":[0.003443761,0.0008973531,0.905485,0.00004346729,0.00008387451,0.00001547638,0.0001074683,0.006742084,0.001640911,0.006628084,0.07462609,0.0002864384],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655799,0.0006012757,0.03243571,0.0004934093,0.000302986,0.0002695596,0.00001338392,0.000008354341,0.0002954084],"genre_scores_gemma":[0.9835925,0.000723171,0.01345372,0.0003950431,0.001186846,0.00002883822,0.0003771571,0.0000191915,0.0002235111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06923224,"threshold_uncertainty_score":0.4795179,"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."}}