{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007604053,0.0002669887,0.000630923,0.00008793665,0.00003717512,0.000005894859,0.0002521848,0.0005761315,0.00007417669],"category_scores_gemma":[0.0000137527,0.0002048815,0.0002284542,0.0000816022,0.0001526815,0.000001329759,0.0001249689,0.00009539281,0.00003116076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003195715,"about_ca_system_score_gemma":0.000330156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003921995,"about_ca_topic_score_gemma":0.00002988699,"domain_scores_codex":[0.9988333,0.0001436741,0.0003079684,0.0003986501,0.0001043772,0.0002120022],"domain_scores_gemma":[0.9991173,0.0000124075,0.0003014331,0.0004016352,0.0001012555,0.00006600918],"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":[0.00005311918,0.00003262016,0.00002233629,0.001139667,0.00056468,0.00003861853,0.00002299752,0.001674175,0.0009889773,0.0001702284,0.0002921359,0.9950004],"study_design_scores_gemma":[0.0003765034,0.0003455467,0.00002834819,0.0002072229,0.0003488492,0.0001083325,0.00001323337,0.00001900072,0.001530792,0.00004248765,0.9966828,0.0002968647],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009919611,0.8989477,0.09906761,0.00001331852,0.0001881603,0.0003732332,0.0001587296,0.000006836126,0.0002524819],"genre_scores_gemma":[0.002585361,0.989979,0.005964004,0.00008199006,0.00009370459,0.00002255966,0.0009519341,0.00004747562,0.0002740074],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9963907,"threshold_uncertainty_score":0.8354825,"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."}}