{"id":"W2237752044","doi":"10.1111/eva.12358","title":"Intense selective hunting leads to artificial evolution in horn size","year":2016,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":232,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Alberta; Alberta Conservation Association","keywords":"Trophy; French horn; Ovis canadensis; Biology; Trait; Selection (genetic algorithm); Natural selection; Evolutionary biology; Ecology; Zoology; Demography; Population","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":[0.0007556738,0.0001396374,0.000243496,0.0004069805,0.0001708322,0.0004273721,0.0002408077,0.0002653917,0.0005530859],"category_scores_gemma":[0.001275759,0.0001060906,0.0002681781,0.0003602063,0.0004466548,0.0001699748,0.0004356156,0.0003481378,0.00008707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002148048,"about_ca_system_score_gemma":0.0001111595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003707006,"about_ca_topic_score_gemma":0.0009709306,"domain_scores_codex":[0.9996334,0.0001394334,0.00002607848,0.00009852228,0.0000620597,0.00004042475],"domain_scores_gemma":[0.9987374,0.0005564755,0.0004194247,0.0001541287,0.00004748589,0.00008510271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006691402,0.0001993652,0.6468886,0.0000750026,0.000464182,0.0006911418,0.0004280648,0.003455395,0.3212664,0.0006400804,0.0001465841,0.02507604],"study_design_scores_gemma":[0.000006697612,0.0001869083,0.9912635,0.000004280993,0.00003687627,0.0003477079,0.00009933775,0.003996927,0.003477341,0.0002320557,0.0003396456,0.000008718696],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994406,0.00005218091,0.000308772,0.000007566274,8.301557e-7,0.000001087985,0.00002461255,0.000003840615,0.0001605343],"genre_scores_gemma":[0.9995139,0.00003533044,0.0002466023,0.00001771101,0.00000303051,0.000004485284,0.00008148073,0.00000295712,0.0000946584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007556738,"threshold_uncertainty_score":0.003996372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008137972547604809,"score_gpt":0.242591211249656,"score_spread":0.2344532387020512,"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."}}