{"id":"W2031468492","doi":"10.1002/cjs.5550350407","title":"Empirical likelihood‐based inference for genetic mixture models","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Empirical likelihood; Inference; Nonparametric statistics; Statistics; Confidence interval; Statistic; Likelihood-ratio test; Mathematics; Econometrics; Genetic model; Computer science; Biology; Genetics; Artificial intelligence; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0205165,0.001174012,0.002326703,0.003224047,0.0008130915,0.002521856,0.004334752,0.00204635,0.00591892],"category_scores_gemma":[0.1259567,0.001720067,0.002036443,0.002212035,0.003581776,0.003963518,0.003114428,0.003795981,0.0009243852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001695393,"about_ca_system_score_gemma":0.001634577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008246153,"about_ca_topic_score_gemma":0.006636955,"domain_scores_codex":[0.9912885,0.006551373,0.0002729944,0.0007721207,0.0009145568,0.000200438],"domain_scores_gemma":[0.8876097,0.1042035,0.002607969,0.003300099,0.001753987,0.0005246743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000262455,0.0001181715,0.005686562,0.0001642611,0.000362196,0.0002888129,0.0002323831,0.633662,0.0008592849,0.3079357,0.0017196,0.04870848],"study_design_scores_gemma":[0.00004146512,0.00001399217,0.0004792627,0.00002196759,0.00001869366,0.00004466668,0.00001328752,0.894497,0.0001704196,0.1041943,0.0004852969,0.00001965365],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006839138,0.0001357658,0.9920359,0.0002084981,0.0000157469,0.00002369512,0.00007761845,0.0001964278,0.0004673079],"genre_scores_gemma":[0.4421858,0.0007716254,0.5493692,0.0003625528,0.0002490891,0.0005449111,0.001422418,0.0004412063,0.004653106],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0205165,"threshold_uncertainty_score":0.1085029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415778019943711,"score_gpt":0.276033311396034,"score_spread":0.2518755311965969,"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."}}