{"id":"W2922767326","doi":"10.1098/rspb.2019.0242","title":"Drivers of geographical patterns of North American language diversity","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Diversity (politics); Variation (astronomy); Globe; Geography; Predictive power; Cultural diversity; Linguistic diversity; Population; Resource (disambiguation); Space (punctuation); Ecology; Economic geography; Computer science; Linguistics; Psychology; Sociology; Demography; Biology; Anthropology","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":[0.0005342313,0.00007123784,0.0001923284,0.00001198006,0.0003682378,0.00001065577,0.0007688166,0.00005572873,0.00008399927],"category_scores_gemma":[0.0001206603,0.00003670428,0.0002769241,0.0006543783,0.002169395,0.00009602992,0.000418507,0.00009358641,0.00000151427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002334065,"about_ca_system_score_gemma":0.00002208379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006353904,"about_ca_topic_score_gemma":0.0001893189,"domain_scores_codex":[0.9989355,0.00002276342,0.0001510882,0.0001878786,0.0004824283,0.0002203785],"domain_scores_gemma":[0.9994125,0.0000576893,0.0003049767,0.0000409163,0.0001346867,0.00004921496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005457801,0.00003431407,0.988499,0.00001302715,0.00001196482,1.664355e-8,0.008277169,0.000003024722,0.001166413,0.001098072,0.00006278721,0.000828777],"study_design_scores_gemma":[0.00007169132,0.0001729641,0.9650397,0.0000153694,0.00001122264,5.702582e-8,0.03344247,0.00004185864,0.0009803376,0.0001048255,0.00005288007,0.00006667706],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972048,0.00003398292,0.000001783807,0.0002385108,0.00005136495,0.0001487442,0.00001284981,0.0000166094,0.00229132],"genre_scores_gemma":[0.999559,0.00007199434,0.0001552729,0.00006577325,0.00003045494,9.986903e-7,5.663162e-7,9.745194e-7,0.0001149027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0251653,"threshold_uncertainty_score":0.9605244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290612020224473,"score_gpt":0.2438595615054128,"score_spread":0.230953441303168,"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."}}