{"id":"W2983201012","doi":"10.1038/s41598-019-52748-8","title":"Contrasting Computational Models of Mate Preference Integration Across 45 Countries","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Evolutionary Psychology and Human Behavior","field":"Psychology","cited_by":1775,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Narodowe Centrum Nauki; Hungarian Scientific Research Fund; Ministerstwo Edukacji i Nauki; National Natural Science Foundation of China; National Foundation for Science and Technology Development","keywords":"Mate choice; Preference; Mating preferences; Mating; Ideal (ethics); Set (abstract data type); Sample (material); Value (mathematics); Pairwise comparison; Computer science; Psychology; Ecology; Biology; Economics; Microeconomics; Developmental psychology","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":[0.003081485,0.0003111707,0.0007427096,0.0009197837,0.0005926117,0.002871485,0.001254046,0.0007844544,0.003625759],"category_scores_gemma":[0.01107054,0.0004923806,0.001449842,0.001120428,0.001334928,0.002337745,0.001481287,0.0007827758,0.0002439273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001265549,"about_ca_system_score_gemma":0.0006344347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01114286,"about_ca_topic_score_gemma":0.008441678,"domain_scores_codex":[0.9990487,0.0006437108,0.00003981884,0.0001435557,0.00005236204,0.00007194284],"domain_scores_gemma":[0.9941292,0.00454984,0.0004466553,0.0004843323,0.0001748395,0.0002151863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004383393,0.0001619605,0.0803002,0.0001145425,0.0005188751,0.000275997,0.00152548,0.7588521,0.0003741593,0.1347238,0.001085657,0.02162887],"study_design_scores_gemma":[0.00007175106,0.00005276594,0.01226395,0.00001579514,0.00007172685,0.00007004225,0.000463602,0.9275996,0.000103789,0.05888931,0.0003730921,0.00002460539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672417,0.0003457073,0.02536831,0.0008398484,0.00001395731,0.00002059678,0.0002024173,0.00005464513,0.005912739],"genre_scores_gemma":[0.992718,0.0001680464,0.006310541,0.0000811764,0.000005977685,0.00003034151,0.0001594146,0.00001106498,0.000515366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01114286,"threshold_uncertainty_score":0.022156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0600574344665355,"score_gpt":0.3473011735792659,"score_spread":0.2872437391127304,"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."}}