{"id":"W1559696064","doi":"10.3389/fgene.2015.00259","title":"A new method for estimating the demographic history from DNA sequences: an importance sampling approach","year":2015,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Plot (graphics); Skyline; Population; Demographic history; Sampling (signal processing); Population size; Statistics; Bayesian probability; Sample size determination; Computer science; Point estimation; Approximate Bayesian computation; Mathematics; Data mining; Artificial intelligence; Inference; Demography","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":[0.0003271861,0.0001481181,0.0001553816,0.00004783409,0.0000679091,0.00002385564,0.0003761681,0.0001816038,0.000004809603],"category_scores_gemma":[0.00007391068,0.00013198,0.00006867391,0.00009204393,0.0000618376,0.000005187632,0.00006193434,0.00009732097,3.118394e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004764616,"about_ca_system_score_gemma":0.0002138176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001062256,"about_ca_topic_score_gemma":0.00007946903,"domain_scores_codex":[0.9989638,0.00009018905,0.0002114728,0.0003724139,0.000148109,0.0002140527],"domain_scores_gemma":[0.9992738,0.00001218417,0.0001211822,0.0003886211,0.00006689129,0.0001372918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003620422,0.00008727662,0.4754436,0.00006126008,0.0002834819,0.000002928508,0.006644155,0.2611166,0.01922611,0.0002105362,0.1396045,0.09695747],"study_design_scores_gemma":[0.004869534,0.0007553782,0.03390687,0.00004382462,0.0003731811,0.00002836041,0.008068647,0.464247,0.006756376,0.04780047,0.4314474,0.001702948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1553612,0.004762158,0.8383988,0.00004708131,0.000993454,0.0002503387,0.00002841457,0.000009490887,0.0001491116],"genre_scores_gemma":[0.04790185,0.00003365279,0.9508541,0.0003222197,0.0004212403,0.00001312287,0.0002916161,0.00001770142,0.0001444662],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4415368,"threshold_uncertainty_score":0.5381989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05506936682669151,"score_gpt":0.2915038201348611,"score_spread":0.2364344533081696,"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."}}