{"id":"W4362662377","doi":"10.1101/2023.04.05.535609","title":"Neutral Drift and Threshold Selection Promote Phenotypic Variation","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Evolvability; Phenotype; Biology; Population; Genetics; Genetic drift; Experimental evolution; Adaptive evolution; Adaptation (eye); Evolutionary biology; Selection (genetic algorithm); Genetic Fitness; Fitness landscape; Neutral mutation; Neutral theory of molecular evolution; Variation (astronomy); Population size; Gene; Genetic variation; Mutation","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005316837,0.0002239575,0.0004102106,0.0002477692,0.0002250959,0.0005806407,0.0003769488,0.0004499328,0.001568571],"category_scores_gemma":[0.001624231,0.0001746387,0.0003222226,0.000204312,0.0007264754,0.0005702201,0.0005983906,0.0005502496,0.0001920216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005683426,"about_ca_system_score_gemma":0.0002476767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003847363,"about_ca_topic_score_gemma":0.0002702034,"domain_scores_codex":[0.9996654,0.00009388907,0.00001277581,0.00006864815,0.0001050685,0.00005422773],"domain_scores_gemma":[0.999466,0.0002608572,0.00007392997,0.00008147186,0.00005058192,0.00006708522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000304447,0.0001236727,0.01045731,0.0001189229,0.00008564427,0.0004213394,0.0001288792,0.05257599,0.8658048,0.05836433,0.0006636823,0.01095092],"study_design_scores_gemma":[0.0002039636,0.0004980299,0.02493488,0.0000243788,0.00006797116,0.0008507714,0.000155624,0.671043,0.214479,0.08500794,0.002670808,0.00006356525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961457,0.0001462776,0.03439137,0.0002806718,0.00002761875,0.00001252166,0.00004384182,0.0001644762,0.003476265],"genre_scores_gemma":[0.9977978,0.0000312962,0.001843489,0.00003463523,0.000004388702,0.000006812657,0.00001308713,0.00001196966,0.0002565506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001568571,"threshold_uncertainty_score":0.005247414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029630022734067,"score_gpt":0.2198517866953247,"score_spread":0.209555486467984,"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."}}