{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001218057,0.0003725123,0.0004721202,0.0001939945,0.004981191,0.002145368,0.004431343,0.001018621,0.00008393225],"category_scores_gemma":[0.0004756341,0.0003677751,0.00007682461,0.000380312,0.0003978134,0.002406903,0.003148516,0.0007529053,0.00015894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001632692,"about_ca_system_score_gemma":0.001972875,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1393441,"about_ca_topic_score_gemma":0.02331915,"domain_scores_codex":[0.9964415,0.0005380716,0.0004404109,0.001211354,0.0006912955,0.000677329],"domain_scores_gemma":[0.9973807,0.00004922941,0.0005502207,0.001399037,0.0004567663,0.0001640755],"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":[0.00002647248,0.00006978547,0.0004941496,0.00003884994,0.00006267217,0.00001485317,0.00266698,0.000004614428,0.0009272096,0.004742628,0.9789807,0.01197106],"study_design_scores_gemma":[0.000331505,0.00003400073,0.01166489,0.0001826734,0.0001947714,0.00001572802,0.0008081248,0.0001148881,0.000005656897,0.00270097,0.9832885,0.0006582738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01062012,0.003623642,0.008904819,0.9263409,0.006751718,0.008288286,0.002151425,0.0004283549,0.03289073],"genre_scores_gemma":[0.5702817,0.0005401319,0.01016817,0.1825038,0.1040622,0.001244668,0.02149563,0.0003633757,0.1093404],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.7438371,"threshold_uncertainty_score":0.9998774,"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."}}