{"id":"W2009290712","doi":"10.1038/nature13301","title":"Genetics of ecological divergence during speciation","year":2014,"lang":"en","type":"article","venue":"Nature","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":336,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Cancer Institute; University of California, Davis","keywords":"Genetic algorithm; Divergence (linguistics); Evolutionary biology; Biology; Ecology; Ecological genetics; Ecological speciation; Genetics; Genetic variation; Gene flow; Gene; Medicine","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":[0.00004796153,0.00005626466,0.00006204269,0.00001709114,0.00004253467,0.000004221708,0.0001140179,0.0006157745,0.00008278913],"category_scores_gemma":[0.00006286686,0.00005201378,0.00004036769,0.00004716934,0.00002392492,0.000001033642,0.00007028029,0.0001954692,0.000005176796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003425433,"about_ca_system_score_gemma":0.000007377437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.9571e-7,"about_ca_topic_score_gemma":0.000006033876,"domain_scores_codex":[0.9995836,0.00002556319,0.00007843681,0.0001335182,0.0001006461,0.00007822037],"domain_scores_gemma":[0.9997134,0.000003900257,0.00005465026,0.0001357865,0.00006381016,0.00002842342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007104675,0.00004270164,0.3723178,0.00003386454,0.00003344649,8.75899e-7,0.00006337033,0.001781635,0.6189856,0.001064308,0.002804397,0.002800961],"study_design_scores_gemma":[0.0002049494,0.00006200422,0.8071807,0.00000210034,0.000008004586,0.000002464324,0.00001066734,0.00003479365,0.1652538,0.0002266921,0.02694326,0.00007060719],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997796,0.0002488598,0.0002350647,0.00006190269,0.0002174195,0.00004316313,0.00001070164,0.000004278078,0.001382592],"genre_scores_gemma":[0.9982001,0.00005793426,0.0009704725,0.0001520433,0.000198185,4.564278e-7,0.00004432697,0.000003111602,0.000373339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4537319,"threshold_uncertainty_score":0.4749417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005394584752000085,"score_gpt":0.2226573180859346,"score_spread":0.2172627333339345,"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."}}