{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002327437,0.00008735727,0.00009408268,0.00001344422,0.0001054305,0.00001270812,0.0003173579,0.0001037159,0.00003112491],"category_scores_gemma":[0.0000674706,0.00005802385,0.00030869,0.0001009691,0.00004698327,0.00000163119,0.0001379359,0.000179162,0.00004621304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003390039,"about_ca_system_score_gemma":0.00006559461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003133912,"about_ca_topic_score_gemma":0.000005112686,"domain_scores_codex":[0.9993211,0.00005502158,0.0001916101,0.00009974775,0.0001855662,0.000146961],"domain_scores_gemma":[0.9995392,0.00001397166,0.0001107623,0.0001875972,0.0001016109,0.00004685447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001107958,0.00005409012,0.001582213,0.00001434049,0.0001550097,0.000001238365,0.0001758803,0.5840709,0.01827241,0.00003967654,0.3952018,0.0003216651],"study_design_scores_gemma":[0.003381503,0.002045393,0.1956362,0.0003080916,0.0001744881,0.0001557396,0.002601178,0.3671316,0.05827034,0.001157621,0.3683164,0.0008214887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880798,0.0008076002,0.007525939,0.002086383,0.0007874244,0.00006341334,0.00001444132,0.00001012732,0.0006248291],"genre_scores_gemma":[0.9876953,0.0001531269,0.0002999266,0.000315731,0.0003205527,0.000001109653,0.000004789392,0.00001174583,0.01119774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2169393,"threshold_uncertainty_score":0.2366144,"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."}}