{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006761012,0.0002158634,0.0002506638,0.001168575,0.0006737731,0.001000139,0.0005592643,0.0007066059,0.004755922],"category_scores_gemma":[0.002877262,0.000200745,0.0003438367,0.0004967514,0.001639784,0.001262752,0.0008591327,0.000715647,0.0003935964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009069618,"about_ca_system_score_gemma":0.000360416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007152222,"about_ca_topic_score_gemma":0.0006534953,"domain_scores_codex":[0.9998065,0.00006523763,0.00000756092,0.00005314987,0.00003814448,0.00002952835],"domain_scores_gemma":[0.999156,0.0004100566,0.0001738578,0.00006989975,0.0000909884,0.00009911024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002396847,0.000131078,0.03454493,0.0001266328,0.00009781979,0.0009962757,0.0006135685,0.1640618,0.04199742,0.6986781,0.001737316,0.0567752],"study_design_scores_gemma":[0.00006284843,0.0002054665,0.03175293,0.00003359584,0.00004392003,0.001203765,0.0005843901,0.2779903,0.00542981,0.6764849,0.006115169,0.00009283455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9480686,0.0002783766,0.04160897,0.001000462,0.00002263171,0.00001245696,0.0001011518,0.00009089711,0.008816502],"genre_scores_gemma":[0.9925959,0.0001542332,0.005795063,0.00006439109,0.000004745259,0.00001545859,0.00004200502,0.00002268332,0.001305596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004755922,"threshold_uncertainty_score":0.01591009,"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."}}