{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003743696,0.0001187864,0.0001428557,0.00005566107,0.0001070632,0.000008490972,0.00007738922,0.0002069706,0.000001003939],"category_scores_gemma":[0.0003279251,0.0001171538,0.00004469925,0.00006855224,0.0001333128,0.000001244813,0.00006330763,0.00005543725,0.000004028083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001898371,"about_ca_system_score_gemma":0.0001015923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002205667,"about_ca_topic_score_gemma":0.00002105912,"domain_scores_codex":[0.9990814,0.0001384982,0.0001984417,0.0002887665,0.0000484918,0.0002444488],"domain_scores_gemma":[0.9993817,0.00004919102,0.00009316188,0.0001231485,0.0002462042,0.0001066618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003131031,0.0002521446,0.5288461,0.00003940933,0.0003226525,0.000002525408,0.0001241538,0.02377787,0.3688128,0.06730896,0.004671272,0.005529041],"study_design_scores_gemma":[0.004241443,0.002650136,0.7175817,0.00002061189,0.0001825328,0.00005164574,0.0001658691,0.07929688,0.003784923,0.1835541,0.007577868,0.0008923338],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4598094,0.0007044422,0.5390061,0.0001531144,0.00008383195,0.0001209559,0.00006701432,0.000006986644,0.00004809508],"genre_scores_gemma":[0.951869,0.00002676626,0.04675727,0.0003027806,0.00006403044,0.00003738815,0.0009125023,0.000009142151,0.00002112216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4922489,"threshold_uncertainty_score":0.4777395,"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."}}