{"id":"W3123335187","doi":"10.18329/09757597/2020/13209","title":"Global Machine-learning Research: a scientometric assessment of global literature during 2009–18","year":2020,"lang":"en","type":"article","venue":"World Digital Libraries - An international journal","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Citation; Subject (documents); Science Citation Index; Index (typography); Citation index; Citation impact; Artificial intelligence; Library science; Per capita; Zhàng; China; Political science; Geography; Computer science; Sociology; Demography; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003579021,0.000145981,0.0002198435,0.0005583063,0.0002578,0.001839956,0.0004747175,0.00006679672,0.0004952152],"category_scores_gemma":[0.001183164,0.0001289641,0.000136028,0.003749401,0.0001922025,0.002572964,0.0001862188,0.0006957932,0.00002285372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004674093,"about_ca_system_score_gemma":0.0009230689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004272738,"about_ca_topic_score_gemma":0.00001271442,"domain_scores_codex":[0.9971733,0.0001023655,0.0006150148,0.0002893834,0.001464939,0.0003549788],"domain_scores_gemma":[0.9978366,0.0001093116,0.000222161,0.0001322499,0.001097887,0.000601784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005214108,0.000268292,0.9597208,0.00004950083,0.00009759442,0.0001895805,0.0006148138,0.0001753023,0.00005026675,0.01730915,0.001785861,0.01921736],"study_design_scores_gemma":[0.00082555,0.002006968,0.9049552,0.001074973,0.00004833629,0.001711888,0.003226981,0.009626926,0.0009008974,0.01920853,0.05592148,0.0004922635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508476,0.0009984473,0.0006063645,0.01097114,0.001088854,0.0001910908,0.0002783027,0.00007154654,0.03494665],"genre_scores_gemma":[0.9959067,0.00007743353,0.001255849,0.0004931084,0.001599843,0.000003470069,0.0001451121,0.00001251011,0.0005059511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05476566,"threshold_uncertainty_score":0.9991962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2428365320297471,"score_gpt":0.4890182123725702,"score_spread":0.2461816803428231,"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."}}