{"id":"W3029387734","doi":"10.1098/rspb.2019.2712","title":"<i>DCDC2</i>READ1 regulatory element: how temporal processing differences may shape language","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Manton Foundation","keywords":"Dimension (graph theory); Population; Articulation (sociology); Stop consonant; Association (psychology); Allele; Psychology; Consonant; Expression (computer science); Biology; Computer science; Mathematics; Sociology; Speech recognition; Genetics; Political science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009127376,0.0001708647,0.0002397042,0.000008429876,0.001392296,0.0002529403,0.001113218,0.0001665531,0.0001718024],"category_scores_gemma":[0.0004148094,0.0000852233,0.0002623472,0.0007434194,0.00208643,0.0003484206,0.0002605013,0.0001972892,0.000005093836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005474387,"about_ca_system_score_gemma":0.00008987176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002808177,"about_ca_topic_score_gemma":0.00001192367,"domain_scores_codex":[0.9980786,0.00004332218,0.0002151103,0.0004334583,0.0007459144,0.0004835534],"domain_scores_gemma":[0.999305,0.00004446773,0.0003017228,0.00004267388,0.0001437927,0.0001623148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003560592,0.0001558468,0.7999341,0.0001665816,0.00005120583,9.286359e-7,0.1115762,0.000003893272,0.02668648,0.01328007,0.01321894,0.03489012],"study_design_scores_gemma":[0.001003615,0.001120391,0.27729,0.0003729094,0.0001371969,0.000002896639,0.6687908,0.01275762,0.01242537,0.003661952,0.02078221,0.00165501],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753644,0.0009299069,0.00000807481,0.01480417,0.00007600898,0.0002798128,0.000006474728,0.0001335414,0.008397561],"genre_scores_gemma":[0.9971963,0.00006147011,0.0006562754,0.001127888,0.0004147312,0.00001379884,0.000001276214,0.000004079188,0.0005242261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5572146,"threshold_uncertainty_score":0.9999077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04267368301760235,"score_gpt":0.2739002192881101,"score_spread":0.2312265362705077,"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."}}