{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001110547,0.0001181007,0.00008814152,0.00006112903,0.0001214399,0.00000611386,0.0001647371,0.0001095183,0.00003415386],"category_scores_gemma":[0.0001333173,0.0001060488,0.00004609498,0.000270548,0.00008530151,0.000005864842,0.00007918947,0.00006638048,0.0001716022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000885722,"about_ca_system_score_gemma":0.0001152776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002488877,"about_ca_topic_score_gemma":0.00009262183,"domain_scores_codex":[0.99899,0.00004722756,0.0002192485,0.0003991929,0.00009856182,0.0002457741],"domain_scores_gemma":[0.9994059,0.00005189486,0.00004784423,0.0002908779,0.000122468,0.00008098262],"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":[0.0001406238,0.0001741524,0.04714569,0.000006278523,0.00002093439,1.803094e-7,0.00008806242,0.0007154305,0.8777966,0.06061374,0.004455846,0.008842502],"study_design_scores_gemma":[0.0002541477,0.000184115,0.9485967,0.00001653043,0.000009583978,0.00001045376,0.00007033197,0.000009694621,0.01236292,0.02536931,0.01287931,0.0002369749],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7032826,0.0002274112,0.2921763,0.00143246,0.00009998127,0.0006382402,0.00005207493,0.00003571909,0.002055177],"genre_scores_gemma":[0.9795607,0.000006394684,0.01808951,0.0001418255,0.00041106,0.0005146709,0.00002220998,0.00001573923,0.001237844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9014509,"threshold_uncertainty_score":0.4324543,"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."}}