{"id":"W3154249250","doi":"10.1103/physreve.103.042415","title":"Robustness and predictability of evolution in bottlenecked populations","year":2021,"lang":"en","type":"article","venue":"Physical review. E","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Predictability; Population; Population size; Bottleneck; Robustness (evolution); Population bottleneck; Adaptation (eye); Natural selection; Evolutionary dynamics; Computer science; Ecology; Biology; Statistics; Selection (genetic algorithm); Mathematics; Artificial intelligence; Genetics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001808598,0.0002212343,0.0006614897,0.0008935018,0.0003736529,0.001633933,0.0005461177,0.000470844,0.0006613233],"category_scores_gemma":[0.01002208,0.0003360678,0.0005857163,0.0004818929,0.001888042,0.001474302,0.001012086,0.0007739755,0.00009592233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008464754,"about_ca_system_score_gemma":0.0003641487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00138493,"about_ca_topic_score_gemma":0.00062411,"domain_scores_codex":[0.9993922,0.0002330834,0.0000511573,0.0001699425,0.00008687912,0.00006690335],"domain_scores_gemma":[0.9955402,0.002734316,0.0009315955,0.000489861,0.0001531101,0.0001509511],"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":[0.0002615518,0.00004348032,0.08538224,0.0004426872,0.0007378268,0.0006528793,0.0007538037,0.7230033,0.04778598,0.09692696,0.0007039363,0.04330532],"study_design_scores_gemma":[0.00003005419,0.0001427967,0.1383463,0.00007478921,0.000155044,0.0004551894,0.0002609265,0.5682333,0.00534822,0.2843148,0.002504626,0.0001339121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9229894,0.001606404,0.07102332,0.0004977466,0.00003251165,0.00001284955,0.0001779982,0.0002814544,0.003378285],"genre_scores_gemma":[0.9975392,0.0003403456,0.001875607,0.0000277863,0.00001243095,0.00000784536,0.00005341213,0.00001789734,0.00012543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001808598,"threshold_uncertainty_score":0.009564877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361656290023981,"score_gpt":0.3241897489078755,"score_spread":0.3105731860076357,"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."}}