{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03457686,0.0009280616,0.0009848304,0.001792815,0.0007761451,0.001764522,0.002897347,0.002179896,0.001700629],"category_scores_gemma":[0.2696053,0.0006920277,0.00135721,0.00141073,0.002844508,0.004250044,0.002017732,0.002385719,0.0003269195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001460659,"about_ca_system_score_gemma":0.00209435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001953847,"about_ca_topic_score_gemma":0.001401516,"domain_scores_codex":[0.9793728,0.01268265,0.00139836,0.00303601,0.002956399,0.000553855],"domain_scores_gemma":[0.4973558,0.4549651,0.01399144,0.02436375,0.007241399,0.002082629],"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":[0.0009718455,0.0003028549,0.09960485,0.0003365409,0.0007507824,0.0003342746,0.0006111626,0.7934633,0.003245586,0.06581571,0.002481735,0.0320814],"study_design_scores_gemma":[0.00006428406,0.0002762676,0.002201277,0.00002968695,0.00003358267,0.00009603056,0.00008781913,0.9608802,0.0033292,0.03253654,0.0004356859,0.00002953469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5883921,0.0002635739,0.4060063,0.0006387592,0.00009249962,0.0001581701,0.001399041,0.001739237,0.001310304],"genre_scores_gemma":[0.9343956,0.0000768958,0.06309737,0.0001863236,0.00003555139,0.0001700102,0.001653438,0.0001833029,0.000201567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03457686,"threshold_uncertainty_score":0.182862,"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."}}