{"id":"W2964238790","doi":"10.1139/gen-2019-0004","title":"The role of dispersal, selection, and timing of sampling on the false discovery rate of loci under selection during geographic range expansion","year":2019,"lang":"en","type":"article","venue":"Genome","topic":"Forest Insect Ecology and Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ; Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service; Ministry of Environment; fRI Research; Alberta Agriculture and Forestry; Canadian Forest Service; Ontario Ministry of Natural Resources and Forestry; Ministry of Environment - Saskatchewan; Compute Canada; Natural Resources Canada; Ministry of Natural Resources","keywords":"Biology; Biological dispersal; Selection (genetic algorithm); Evolutionary biology; Directional selection; Locus (genetics); Range (aeronautics); Background selection; Population; Local adaptation; Genetic variation; Allele; Genetics; Machine learning; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08630387,0.0008497002,0.001041169,0.001334199,0.001084646,0.002486313,0.002324662,0.002185143,0.0008030143],"category_scores_gemma":[0.2379209,0.0007344766,0.00125762,0.001186213,0.00417611,0.002533413,0.001502807,0.002485446,0.0001743409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00186993,"about_ca_system_score_gemma":0.001457425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004163897,"about_ca_topic_score_gemma":0.003915034,"domain_scores_codex":[0.9463378,0.04230711,0.002501843,0.004263008,0.003254988,0.0013353],"domain_scores_gemma":[0.5300898,0.4237021,0.02397667,0.01720529,0.003579606,0.001446492],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002230879,0.0003103373,0.6103688,0.000491887,0.002509666,0.001906823,0.002003294,0.2504154,0.03434576,0.02249124,0.001053555,0.07187236],"study_design_scores_gemma":[0.0003318363,0.001197887,0.2469974,0.000186923,0.001266832,0.003265762,0.0004525834,0.6795363,0.03481175,0.0299857,0.001615531,0.0003515622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7601622,0.00143082,0.2330261,0.002134773,0.0001283035,0.0001542844,0.0002765246,0.000591488,0.002095493],"genre_scores_gemma":[0.9841456,0.0001314663,0.01500657,0.0002196075,0.0000196018,0.00006554534,0.00007907528,0.00004426737,0.0002883739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9136961,"threshold_uncertainty_score":0.4564239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007413232848192533,"score_gpt":0.1964700336401372,"score_spread":0.1890568007919446,"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."}}