{"id":"W2253273205","doi":"10.1093/molbev/msv255","title":"Computationally Efficient Composite Likelihood Statistics for Demographic Inference","year":2015,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":159,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Bootstrapping (finance); Inference; Statistical inference; Computer science; Model selection; Selection (genetic algorithm); Statistics; Biology; Maximum likelihood; Quasi-maximum likelihood; Machine learning; Population; Estimation theory; Artificial intelligence; Econometrics; Likelihood function; Algorithm; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01120738,0.0009425244,0.001370811,0.002572102,0.00090529,0.002089365,0.002721212,0.001214572,0.009267987],"category_scores_gemma":[0.06820702,0.0008153648,0.001436964,0.002906829,0.001918586,0.003743496,0.002860038,0.004542477,0.003225308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140489,"about_ca_system_score_gemma":0.002759623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00207942,"about_ca_topic_score_gemma":0.002683344,"domain_scores_codex":[0.9954336,0.002738422,0.0002316294,0.0004396769,0.001026664,0.0001299504],"domain_scores_gemma":[0.9634168,0.0293429,0.00138956,0.003070681,0.002323128,0.0004569373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002172163,0.0001446532,0.004026002,0.0003201477,0.000170969,0.0002644047,0.0002644096,0.2416862,0.003449529,0.5255367,0.009294634,0.2146252],"study_design_scores_gemma":[0.00004185558,0.00003339772,0.0006439712,0.0000363591,0.00001852315,0.0001318769,0.00003923925,0.6716328,0.001443381,0.3195352,0.006400838,0.0000424965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001222286,0.00006225822,0.9976421,0.00009663189,0.00001771406,0.00002101038,0.00009129114,0.0003866526,0.0004599802],"genre_scores_gemma":[0.04931873,0.0001777362,0.948073,0.0001110382,0.00009886756,0.0002615876,0.0005032255,0.0004395738,0.001016144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01120738,"threshold_uncertainty_score":0.05927098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01107193007336607,"score_gpt":0.2904943063781092,"score_spread":0.2794223763047431,"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."}}