{"id":"W3194095174","doi":"10.1126/sciadv.abd9223","title":"Exploring correlations in genetic and cultural variation across language families in northeast Asia","year":2021,"lang":"en","type":"article","venue":"Science Advances","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Ministry of Education, Culture, Sports, Science and Technology; Research Organization of Information and Systems","keywords":"Variation (astronomy); Lexicon; Grammar; Genetic variation; Evolutionary biology; Linguistics; Geography; Biology; Natural language processing; Computer science; Genetics; Gene","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":[0.0008810204,0.0002172333,0.0003049271,0.001200478,0.0007603832,0.0006522438,0.0002832599,0.0001761978,0.001405444],"category_scores_gemma":[0.00302808,0.0001401495,0.0003385872,0.00199801,0.0007224961,0.0003485049,0.0009790923,0.0002877418,0.0001027066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000510727,"about_ca_system_score_gemma":0.0005796702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04270067,"about_ca_topic_score_gemma":0.0702046,"domain_scores_codex":[0.9993954,0.0002012903,0.00005455705,0.0002025195,0.00006315453,0.00008312059],"domain_scores_gemma":[0.9982875,0.0006922196,0.0004004407,0.0001977392,0.0002553827,0.0001667374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003765375,0.00001133369,0.9875974,0.00001688612,0.0001584806,0.0001589777,0.004055194,0.0001292788,0.002477942,0.0001340909,0.00005323157,0.005169559],"study_design_scores_gemma":[0.000001034397,0.00001093809,0.9977282,0.00000658329,0.00003123605,0.00007051005,0.001642478,0.0001890836,0.0001301697,0.0000522548,0.0001345668,0.000003117286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994277,0.00004049556,0.0001686572,0.00001526357,6.363268e-7,0.000001963025,0.0001032032,0.000001349948,0.0002407149],"genre_scores_gemma":[0.9994906,0.00003848274,0.0002065401,0.000009182687,7.014068e-7,0.000004178826,0.000175004,0.000002063166,0.00007328255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04270067,"threshold_uncertainty_score":0.08490425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03305923283455072,"score_gpt":0.333285872341584,"score_spread":0.3002266395070333,"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."}}