{"id":"W4388682255","doi":"10.1098/rsif.2023.0424","title":"Evolutionary rescue on genotypic fitness landscapes","year":2023,"lang":"en","type":"article","venue":"Journal of The Royal Society Interface","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Biology; Mutation; Adaptation (eye); Trait; Mutation rate; Genetic Fitness; Evolutionary biology; Evolutionary dynamics; Selection (genetic algorithm); Fitness landscape; Evolvability; Natural selection; Extinction (optical mineralogy); Genetics; Gene; Population; Computer science; Artificial intelligence","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":[0.001016085,0.000503439,0.0006710245,0.0008176955,0.0004331157,0.0009983506,0.0008432922,0.001033286,0.003231027],"category_scores_gemma":[0.009002102,0.0002576431,0.000689557,0.0002798052,0.001445512,0.001568423,0.001037129,0.0007539543,0.0002910497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006132945,"about_ca_system_score_gemma":0.000276574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007073699,"about_ca_topic_score_gemma":0.000412025,"domain_scores_codex":[0.9995362,0.0002268898,0.00001687718,0.00008483793,0.00006907222,0.0000660656],"domain_scores_gemma":[0.9971125,0.001926079,0.0003648524,0.0002832456,0.0001233977,0.0001899897],"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":[0.0001043702,0.00004286203,0.007246949,0.0001177222,0.00008467957,0.000677312,0.0002048555,0.6690547,0.008394347,0.2920074,0.001666463,0.02039835],"study_design_scores_gemma":[0.00001936943,0.00007654961,0.003566157,0.00001723105,0.00001740547,0.0004827272,0.00006267765,0.8448919,0.0008446593,0.1487481,0.001244861,0.00002845149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7670394,0.0004682978,0.2194747,0.0009587968,0.00003355126,0.00004571617,0.0002205256,0.0002699317,0.01148902],"genre_scores_gemma":[0.9910642,0.0001649973,0.006532981,0.00008992435,0.00001705317,0.00006077812,0.00010057,0.00004181456,0.001927768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003231027,"threshold_uncertainty_score":0.01080883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007043407224239899,"score_gpt":0.2519440389657455,"score_spread":0.2449006317415056,"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."}}