{"id":"W2968705772","doi":"10.1073/pnas.1910949116","title":"Scenarios where increased population size can enhance cumulative cultural evolution are likely common","year":2019,"lang":"en","type":"letter","venue":"Proceedings of the National Academy of Sciences","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Capilano University","funders":"","keywords":"Copying; Population size; Population; Evolutionary biology; Psychology; Cognitive psychology; Computer science; Geography; Biology; Demography; Genetics; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001138744,0.0002302683,0.0003762387,0.0001281461,0.0008406946,0.0001001273,0.001209972,0.0006720066,0.00006710357],"category_scores_gemma":[0.001691179,0.0001517096,0.0002039294,0.001059537,0.001125667,0.001139596,0.0001279117,0.0008479023,0.000006568658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005763062,"about_ca_system_score_gemma":0.0001351294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005604247,"about_ca_topic_score_gemma":0.00009279724,"domain_scores_codex":[0.9957386,0.0000814313,0.0005076172,0.0004475908,0.002868215,0.0003565838],"domain_scores_gemma":[0.9969778,0.0003234756,0.001734013,0.00001725946,0.0009081074,0.00003936288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00006592287,0.0001012578,0.09273013,0.0009166293,0.0001276374,1.63887e-7,0.0115763,0.0002301017,0.02809956,0.0202765,0.8454399,0.0004358923],"study_design_scores_gemma":[0.0009261431,0.0002103092,0.8009622,0.004793989,0.0003138498,0.00001541323,0.01551905,0.0008920947,0.009782452,0.08841198,0.07644303,0.00172948],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5919374,0.0007149283,3.308694e-7,0.3959574,0.0001827297,0.001023502,0.0001440569,0.00006343354,0.009976187],"genre_scores_gemma":[0.9713627,0.00005372221,0.0001460741,0.02377497,0.001389826,0.00001758002,0.00000619493,0.000008975067,0.003239998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7689969,"threshold_uncertainty_score":0.8471982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313865859135796,"score_gpt":0.3283910892439754,"score_spread":0.2970045033303957,"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."}}