{"id":"W2939850460","doi":"10.1002/ece3.5066","title":"Environmental and demographic drivers of male mating success vary across sequential reproductive episodes in a polygynous breeder","year":2019,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Saskatchewan","keywords":"Harem; Mating; Sexual selection; Reproductive success; Polygyny; Mating system; Biology; Seasonal breeder; Population; Ecology; Ungulate; Operational sex ratio; Selection (genetic algorithm); Competition (biology); Resource Acquisition Is Initialization; Demography; Resource allocation; Habitat","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003554205,0.0001129447,0.000200736,0.0003994171,0.0003125923,0.0003604438,0.0002618521,0.0001709414,0.0009806541],"category_scores_gemma":[0.0007676419,0.0001608053,0.0002265002,0.0004336854,0.0002868224,0.000230147,0.0003402283,0.0002465711,0.00009540936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002954632,"about_ca_system_score_gemma":0.0002196843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05116012,"about_ca_topic_score_gemma":0.1523241,"domain_scores_codex":[0.9998434,0.0000424268,0.000008675524,0.00006475296,0.00001225899,0.0000284786],"domain_scores_gemma":[0.9995486,0.00009689748,0.0001777514,0.00005172396,0.00003066242,0.00009439126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002329353,0.00001669789,0.9973683,0.00000281854,0.00004891125,0.00003204819,0.0002001265,0.000110343,0.0007304542,0.00002277365,0.00003987374,0.001404475],"study_design_scores_gemma":[3.661234e-7,0.000005949345,0.9995643,5.625386e-7,0.000004126717,0.00002056585,0.00008273838,0.0002815393,0.0000115211,0.000008751578,0.00001865974,9.273198e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999809,0.0000187497,0.00007026749,0.000006637497,2.842471e-7,8.544977e-7,0.00005683742,0.000001586516,0.00003602182],"genre_scores_gemma":[0.999648,0.00001436839,0.0001205364,0.000005513941,8.739303e-7,0.000001689041,0.0001467995,0.00000106268,0.00006128133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05116012,"threshold_uncertainty_score":0.1017247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00849571630158056,"score_gpt":0.2133952913696418,"score_spread":0.2048995750680612,"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."}}