{"id":"W1939134820","doi":"10.1111/evo.12237","title":"INTEGRATING LANDSCAPE GENOMICS AND SPATIALLY EXPLICIT APPROACHES TO DETECT LOCI UNDER SELECTION IN CLINAL POPULATIONS","year":2013,"lang":"en","type":"article","venue":"Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Lethbridge","funders":"","keywords":"Cline (biology); Biology; Population genomics; Genomics; Selection (genetic algorithm); Population; Local adaptation; Evolutionary biology; Locus (genetics); Directional selection; Genome; Genetics; Genetic variation; Artificial intelligence; Computer science; Gene","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":[0.00006361611,0.00007550025,0.00006147641,0.00006380818,0.00008265019,0.00002724974,0.00004236147,0.0001039944,0.00002604932],"category_scores_gemma":[0.00002853173,0.00007813103,0.00002007214,0.00009638554,0.00001005221,0.000007736222,0.00004172234,0.00005901056,0.00001031665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002989624,"about_ca_system_score_gemma":0.00002709548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003425087,"about_ca_topic_score_gemma":0.002237268,"domain_scores_codex":[0.9994983,0.00003545394,0.0001173498,0.0001836177,0.0000537797,0.0001114818],"domain_scores_gemma":[0.999822,0.000003617774,0.00003749628,0.00006242183,0.00003014462,0.00004438488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009273483,0.00003622757,0.5317385,0.00002816025,0.00003236299,2.42914e-7,0.0006981606,0.07522874,0.3701992,0.004707372,0.001419991,0.01581842],"study_design_scores_gemma":[0.0002892381,0.0001172754,0.9859829,0.000006510088,0.000009437121,0.000007966805,0.0004065923,0.007358816,0.002387818,0.002677938,0.000600743,0.0001547976],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093499,0.00008086793,0.08996741,0.0001744866,0.00004467803,0.0001809262,0.000003180916,0.000007327019,0.0001912407],"genre_scores_gemma":[0.9857807,0.000005370135,0.01383368,0.00007008893,0.0001029169,0.0000123516,0.00007270294,0.000005951664,0.0001162823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4542444,"threshold_uncertainty_score":0.3186091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04389208798849478,"score_gpt":0.2333120453826616,"score_spread":0.1894199573941668,"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."}}