{"id":"W2952700157","doi":"10.1534/genetics.119.302000","title":"Computational Complexity as an Ultimate Constraint on Evolution","year":2019,"lang":"en","type":"article","venue":"Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; McGill University","keywords":"Fitness landscape; Epistasis; Local optimum; Evolutionary dynamics; Constraint (computer-aided design); Fitness approximation; Reciprocal; Natural selection; Selection (genetic algorithm); Computer science; Biology; Mathematical optimization; Artificial intelligence; Mathematics; Fitness function; Population; Genetic algorithm; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001048651,0.0001417399,0.0001032108,0.00003745007,0.00007000826,0.00002246121,0.0001550356,0.0001251567,0.0001575215],"category_scores_gemma":[0.00002480553,0.0001512614,0.00005739314,0.00005571327,0.0001346354,0.000001904525,0.00005678176,0.0000768078,0.0004127993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002958677,"about_ca_system_score_gemma":0.0001439441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007669028,"about_ca_topic_score_gemma":0.0000292901,"domain_scores_codex":[0.9990242,0.00005892855,0.0001755751,0.0003381075,0.0001878607,0.0002153955],"domain_scores_gemma":[0.999328,0.00000738689,0.00006161745,0.0003418922,0.0001135987,0.0001475675],"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":[0.0003270616,0.0006741613,0.03427995,0.00005382511,0.0001433628,0.000004853707,0.0001656354,0.6174291,0.2400483,0.09613921,0.005644341,0.005090218],"study_design_scores_gemma":[0.004922341,0.005555575,0.2374927,0.000043266,0.00005976304,0.000213435,0.000620263,0.6323172,0.02010855,0.04921342,0.04778779,0.001665686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751541,0.00007741103,0.01809623,0.0001595566,0.0002142586,0.000219361,0.00006258978,0.00002509025,0.005991394],"genre_scores_gemma":[0.9869787,0.00001772079,0.01087381,0.000835304,0.0001039817,0.000004518658,0.0006175363,0.00001917568,0.0005493187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2199397,"threshold_uncertainty_score":0.6168261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314013834587951,"score_gpt":0.2776775586156691,"score_spread":0.2645374202697896,"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."}}