{"id":"W2768067130","doi":"10.1111/evo.13390","title":"Digest: Gene duplication and social evolution-Using big, open data to answer big, open questions","year":2017,"lang":"en","type":"letter","venue":"Evolution","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biology; Big data; Gene duplication; Data science; Evolutionary biology; Gene; Genetics; Computer science; Data mining","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":[0.01010543,0.00111738,0.001797794,0.001297817,0.004146588,0.006587178,0.002577676,0.04157607,0.008823177],"category_scores_gemma":[0.04619278,0.0007597404,0.001210151,0.0009844023,0.005497734,0.007762615,0.003475409,0.04333954,0.007364567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0033468,"about_ca_system_score_gemma":0.003104737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003684426,"about_ca_topic_score_gemma":0.005810553,"domain_scores_codex":[0.9962178,0.001278797,0.000394555,0.0006009465,0.001151965,0.0003559789],"domain_scores_gemma":[0.9550304,0.03332419,0.002089248,0.001641542,0.00426625,0.003648409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004922139,0.00001911784,0.0005326938,0.00003376104,0.00002335848,0.0003050709,0.0001448464,0.00003676376,0.0001296261,0.003063644,0.9881137,0.007548191],"study_design_scores_gemma":[0.0002596041,0.00008741237,0.003389612,0.0006921053,0.00008481772,0.001205717,0.001274226,0.001790971,0.0005640052,0.08274789,0.9077634,0.000140217],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0002409957,0.001312952,0.0002935333,0.9708873,0.02607958,0.00001318436,0.0001649029,0.00004839009,0.0009591382],"genre_scores_gemma":[0.002627268,0.001083666,0.000502316,0.8870296,0.1017467,0.00005865054,0.00008979717,0.00005520853,0.006806817],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.04157607,"threshold_uncertainty_score":0.05344325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1610371532806406,"score_gpt":0.4023428330868896,"score_spread":0.2413056798062489,"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."}}