{"id":"W2294198175","doi":"10.1111/mec.13606","title":"Genomics of local adaptation with gene flow","year":2016,"lang":"en","type":"review","venue":"Molecular Ecology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":535,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Gene flow; Adaptation (eye); Local adaptation; Biology; Natural selection; Genomics; Gene; Genetic architecture; Selection (genetic algorithm); Genetics; Evolutionary biology; Genome; Genetic variation; Phenotype; Population; Computer science; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005309643,0.0007621312,0.001261619,0.002057833,0.0003355359,0.001338738,0.000850804,0.001554944,0.002596854],"category_scores_gemma":[0.0009236733,0.0002936009,0.0006055284,0.002316676,0.0008576722,0.001889184,0.0008855556,0.00141494,0.001383204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009736221,"about_ca_system_score_gemma":0.001514378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001366031,"about_ca_topic_score_gemma":0.001248733,"domain_scores_codex":[0.9997959,0.00003457014,0.00002277333,0.00005976945,0.00006565355,0.00002141777],"domain_scores_gemma":[0.9996277,0.00019162,0.00005747373,0.00001700728,0.00007275171,0.00003336265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006147946,0.00004613654,0.0007425202,0.01975285,0.0001383921,0.0003543092,0.0001568978,0.0009583439,0.004733887,0.01737051,0.0186975,0.9369872],"study_design_scores_gemma":[0.000008931555,0.00005775245,0.003305702,0.003617527,0.0001491932,0.001839625,0.0001176401,0.0001991549,0.001580251,0.009915816,0.97917,0.00003844968],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002982111,0.9963457,0.0004621588,0.0005563238,0.0001917353,0.000004760788,0.00003193489,0.00001349374,0.002095588],"genre_scores_gemma":[0.001546976,0.9970944,0.0003224801,0.0002026811,0.0001405513,0.000006244296,0.00004502226,0.000002625443,0.0006389591],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002596854,"threshold_uncertainty_score":0.008687317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407778376345648,"score_gpt":0.2420695772022943,"score_spread":0.2279917934388378,"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."}}