{"id":"W3090102164","doi":"10.1002/gepi.22367","title":"Detecting rare copy number variants from Illumina genotyping arrays with the CamCNV pipeline: Segmentation of  <i>z</i> ‐scores improves detection and reliability","year":2020,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health; Cancer Research UK; Government of Canada; Fondation du cancer du sein du Québec; Canadian Institutes of Health Research; Genome Canada","keywords":"Copy-number variation; Genotyping; 1000 Genomes Project; Pipeline (software); Metric (unit); Genome; Computational biology; Replicate; Biology; Sensitivity (control systems); False discovery rate; Genetics; Computer science; Statistics; Genotype; Single-nucleotide polymorphism; Mathematics; Gene","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":[0.0003370213,0.0001598411,0.0002584808,0.00001076224,0.0001463574,0.00000798275,0.0001252494,0.0001408628,0.00002226555],"category_scores_gemma":[0.000375874,0.0001162047,0.0000514091,0.00006899513,0.0001863867,0.000005520488,0.00009584077,0.000110883,0.000003124828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001124004,"about_ca_system_score_gemma":0.00004641983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005674065,"about_ca_topic_score_gemma":0.0001845233,"domain_scores_codex":[0.9985667,0.0003556607,0.0003936669,0.000411168,0.00005548857,0.0002172908],"domain_scores_gemma":[0.9991019,0.0002089203,0.0002776463,0.0002513963,0.00009158925,0.00006848147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002651426,0.00001571558,0.09501878,0.00005789694,0.00007474438,0.000001040639,0.00108263,0.002323927,0.8935801,0.00002111819,0.0001008968,0.007457992],"study_design_scores_gemma":[0.001453534,0.001025462,0.2945138,0.00002863164,0.0002258172,0.00007216937,0.003959193,0.003970116,0.6912166,0.001808101,0.00121323,0.0005132787],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8543096,0.00049136,0.1442336,0.0005827033,0.00006733546,0.0002154969,0.00003435114,0.00001001936,0.00005554912],"genre_scores_gemma":[0.9804655,0.0001761079,0.01817647,0.0007990782,0.0002672365,0.00003489808,0.00004533748,0.00001666277,0.00001873296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2023635,"threshold_uncertainty_score":0.4738689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120413373899597,"score_gpt":0.233728829391248,"score_spread":0.2216874920012883,"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."}}