{"id":"W2996629289","doi":"10.1534/genetics.119.302890","title":"Genetic Paths to Evolutionary Rescue and the Distribution of Fitness Effects Along Them","year":2019,"lang":"en","type":"article","venue":"Genetics","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Institute of General Medical Sciences; Agence Nationale de la Recherche","keywords":"Biology; Adaptation (eye); Selection (genetic algorithm); Evolutionary biology; Genetic Fitness; Epistasis; Mutation; Genetics; Human evolutionary genetics; Local adaptation; Mutation rate; Biological evolution; Population; Gene; Genome; Demography; Computer science; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001745908,0.0001439659,0.0001650348,0.00001854778,0.0000644282,0.00001220199,0.0002017357,0.0001245803,0.000008550687],"category_scores_gemma":[0.00008329146,0.0001122744,0.00007263249,0.0001142438,0.000168583,0.000001394158,0.0002195234,0.0000651157,0.00002426058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001300582,"about_ca_system_score_gemma":0.00005716568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001798659,"about_ca_topic_score_gemma":0.0000279181,"domain_scores_codex":[0.9989866,0.000112168,0.0002310629,0.00028794,0.0001679889,0.0002142584],"domain_scores_gemma":[0.9992239,0.00003863623,0.0000603708,0.0004807423,0.0001142319,0.0000820936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001243946,0.0003057161,0.4713621,0.0005509196,0.0003688085,0.000006677697,0.000727183,0.05240131,0.4297472,0.008987132,0.01043472,0.02386426],"study_design_scores_gemma":[0.002916787,0.000513638,0.9460146,0.00004637332,0.00007981897,0.00006173331,0.0001342179,0.01074247,0.02395917,0.001084057,0.01404247,0.0004047155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877943,0.001878179,0.009147101,0.0002114814,0.0001953417,0.0005176229,0.00003973949,0.000007798175,0.0002084708],"genre_scores_gemma":[0.9967107,0.0004543408,0.002014925,0.0002094959,0.00009592772,0.00002524883,0.00008635446,0.00001766079,0.0003853601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4746524,"threshold_uncertainty_score":0.4578416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003341003828953052,"score_gpt":0.2074188150261911,"score_spread":0.2040778111972381,"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."}}