{"id":"W4297545272","doi":"10.3389/fevo.2022.928277","title":"Using fish to understand how cities affect sexual selection before and after mating","year":2022,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Ciência sem Fronteiras; Universidade do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Biology; Sexual selection; Poecilia; Guppy; Mating; Ecology; Mate choice; Zoology; Sperm; Mating preferences; Trait; Urbanization; Reproductive success; Natural selection; Population; Demography; Genetics; Fish <Actinopterygii>; Fishery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002045771,0.0001901486,0.000167026,0.0002733868,0.0002643661,0.0002564904,0.0001745586,0.0002478957,0.001432808],"category_scores_gemma":[0.0003233301,0.0001432248,0.0002675774,0.0002698298,0.000408267,0.0003028261,0.0003142557,0.0003354678,0.0001394171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005519955,"about_ca_system_score_gemma":0.0001980569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004944407,"about_ca_topic_score_gemma":0.01628527,"domain_scores_codex":[0.9998877,0.00001709215,0.000004312992,0.00004582628,0.00001945905,0.00002562539],"domain_scores_gemma":[0.9998329,0.00003330248,0.00007329932,0.00001420219,0.00001734414,0.00002885582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006904156,0.000222362,0.624568,0.0001201063,0.0002124002,0.0002624779,0.001181016,0.001492873,0.3467089,0.0007350395,0.0002491681,0.0235571],"study_design_scores_gemma":[0.000008857263,0.0002681886,0.9947551,0.000004606588,0.00002660111,0.00004217122,0.0003491647,0.0006373068,0.00329685,0.0001829111,0.0004180112,0.00001024186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987454,0.00009267422,0.0003968291,0.00003437738,0.000002611219,0.000007503859,0.00008394636,0.000005348353,0.0006313104],"genre_scores_gemma":[0.9978542,0.0001299523,0.001092201,0.00007453616,0.000003091651,0.00002737603,0.0001078238,0.000004411851,0.0007064109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004944407,"threshold_uncertainty_score":0.00983125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788539674085698,"score_gpt":0.2197295291116702,"score_spread":0.2018441323708132,"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."}}