{"id":"W2946737733","doi":"10.1109/tcbb.2022.3177956","title":"Testing Multispecies Coalescent Simulators Using Summary Statistics","year":2022,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institute of General Medical Sciences; National Science Foundation of Sri Lanka; Simons Foundation; Division of Mathematical Sciences; National Institutes of Health; National Science Foundation","keywords":"Coalescent theory; Inference; Metric (unit); Computer science; Tree (set theory); Process (computing); Scale (ratio); Data mining; Theoretical computer science; Machine learning; Artificial intelligence; Mathematics; Biology; Programming language; Engineering; Gene; Phylogenetic tree; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001269163,0.0001573573,0.0001409553,0.00008097307,0.0007281949,0.00001757191,0.0001354756,0.00006545326,0.00001343255],"category_scores_gemma":[0.00002880762,0.0001572292,0.00004442597,0.0001060412,0.0001549508,0.000002061249,0.0000396127,0.000142298,0.000002247058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000286181,"about_ca_system_score_gemma":0.00008631425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001202762,"about_ca_topic_score_gemma":0.000005771618,"domain_scores_codex":[0.9991725,0.00005533744,0.0002953126,0.0001821701,0.0001047756,0.0001898754],"domain_scores_gemma":[0.9994236,0.0001682317,0.0001078927,0.0001502844,0.00009417648,0.00005578517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000129449,0.0001893969,0.005893333,0.00005111615,0.0002412047,0.000002343301,0.0003514609,0.9399942,0.02328832,0.0002983111,0.0003676794,0.02919315],"study_design_scores_gemma":[0.002965799,0.002797756,0.01406636,0.00003430059,0.0001886975,0.0002792346,0.002364799,0.9454516,0.005930394,0.006539138,0.01808578,0.001296204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6065323,0.0002666689,0.3908886,0.00006998218,0.0004663659,0.0002070437,0.001491909,0.00001082441,0.00006630311],"genre_scores_gemma":[0.8623349,0.0000594571,0.1369215,0.0003875791,0.00005107416,0.00001346779,0.0001813439,0.00001102298,0.00003971017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2558026,"threshold_uncertainty_score":0.641162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03064823498408768,"score_gpt":0.276797289549905,"score_spread":0.2461490545658173,"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."}}