{"id":"W2170692702","doi":"10.1093/sysbio/syr012","title":"History Can Matter: Non-Markovian Behavior of Ancestral Lineages","year":2011,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"U.S. National Library of Medicine; National Institute of General Medical Sciences; National Institutes of Health; Université de Montréal","keywords":"Biology; Fixation (population genetics); Population; Population genetics; Evolutionary biology; Natural selection; Interspecific competition; Selection (genetic algorithm); Genetic drift; Coalescent theory; Mutation; Mutation rate; Genetics; Lineage (genetic); Effective population size; Molecular evolution; Genetic variation; Phylogenetics; Gene; Ecology","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":[0.001820562,0.0001559065,0.0004874246,0.0005895633,0.001101509,0.001436997,0.0009405997,0.001022957,0.003748637],"category_scores_gemma":[0.01122561,0.000382032,0.0005833975,0.0004584287,0.001763692,0.00284144,0.0008467064,0.001285533,0.0002297221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001476799,"about_ca_system_score_gemma":0.0006754833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009759215,"about_ca_topic_score_gemma":0.009148617,"domain_scores_codex":[0.9996833,0.0001318804,0.00001154448,0.00008624203,0.0000368877,0.00005012466],"domain_scores_gemma":[0.996949,0.00196377,0.0003219614,0.0002926174,0.0001562067,0.000316534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004299424,0.0001169778,0.1280218,0.0002020075,0.0002249155,0.001465128,0.004337472,0.4243061,0.01184996,0.4063627,0.002872943,0.01981008],"study_design_scores_gemma":[0.0000619459,0.00007295963,0.03044481,0.00005037635,0.00006621703,0.0003160996,0.0005383945,0.74159,0.0009269068,0.2239745,0.001870168,0.0000876666],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969671,0.0003162511,0.02265242,0.001252586,0.00002000636,0.00001828497,0.0001630869,0.00009072645,0.005815631],"genre_scores_gemma":[0.9957234,0.0001499721,0.00296113,0.0001034194,0.00001122019,0.00001900302,0.000116046,0.00003054676,0.0008853169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009759215,"threshold_uncertainty_score":0.01940483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936577896822259,"score_gpt":0.2382467581779188,"score_spread":0.2188809792096962,"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."}}