{"id":"W2162159470","doi":"10.1534/genetics.105.054650","title":"Quantitative Trait Linkage Analysis Using Gaussian Copulas","year":2006,"lang":"en","type":"article","venue":"Genetics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Eye Institute; National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Quantitative trait locus; Copula (linguistics); Multivariate statistics; Type I and type II errors; Covariate; Multivariate normal distribution; Trait; Linkage (software); Statistics; Gaussian; Computer science; Mathematics; Econometrics; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009102553,0.0001440618,0.0001689552,0.00009280867,0.000130446,0.00004165134,0.0001522561,0.000125321,0.00005974108],"category_scores_gemma":[0.00001046708,0.0001431538,0.0001590162,0.0002216806,0.00006377841,0.000001249126,0.00006566842,0.00005003264,0.00001161896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006641869,"about_ca_system_score_gemma":0.00003403013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000115576,"about_ca_topic_score_gemma":0.0001794709,"domain_scores_codex":[0.9991004,0.00004007639,0.0001846754,0.0002970151,0.0001312158,0.0002465717],"domain_scores_gemma":[0.9995638,0.000006916222,0.00007404038,0.0002271158,0.00006199939,0.00006611223],"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":[0.00005585343,0.0001249574,0.08721422,0.00002692038,0.0006303768,0.00001922551,0.0001090158,0.03791622,0.8668696,0.0007833663,0.005736009,0.000514254],"study_design_scores_gemma":[0.003084489,0.001946147,0.5053837,0.0000610608,0.003842878,0.00008286024,0.002049821,0.04476934,0.2754759,0.001746094,0.1587297,0.002827961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801992,0.0007627673,0.01537865,0.00003842966,0.00007640946,0.00006933677,0.0001255972,0.00001202329,0.003337568],"genre_scores_gemma":[0.9811256,0.00006665547,0.017219,0.00009967153,0.0001719193,0.000001221773,0.0002251864,0.00001058626,0.001080171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5913937,"threshold_uncertainty_score":0.5837643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240050049900353,"score_gpt":0.2678866104725676,"score_spread":0.2438816054825323,"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."}}