{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006609106,0.0005948954,0.0008087608,0.05568119,0.001124102,0.004293741,0.0005619167,0.0006915705,0.001636093],"category_scores_gemma":[0.02127963,0.0001596439,0.0008253012,0.1095024,0.00101234,0.004775173,0.002325875,0.0005065676,0.000750732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002521683,"about_ca_system_score_gemma":0.002794644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003149531,"about_ca_topic_score_gemma":0.004383117,"domain_scores_codex":[0.9939882,0.0008577391,0.001149495,0.0006779747,0.002911959,0.0004147028],"domain_scores_gemma":[0.9773653,0.005757662,0.007815343,0.001105583,0.006814525,0.00114169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002824184,0.0001324009,0.6556661,0.004493596,0.000452649,0.001102245,0.009048454,0.002353329,0.001935697,0.008053309,0.0254724,0.2910073],"study_design_scores_gemma":[0.000007210495,0.0001285918,0.9191517,0.0005926126,0.0001161338,0.0008008282,0.0046521,0.001041085,0.0008836035,0.001287997,0.07129993,0.00003831944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8455408,0.04499358,0.002521365,0.00492468,0.0004543521,0.000258869,0.04284877,0.0004183932,0.05803913],"genre_scores_gemma":[0.943603,0.02183563,0.002786194,0.0004463617,0.0006610421,0.0001892581,0.02583926,0.0001012607,0.004537921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9443188,"threshold_uncertainty_score":0.03495276,"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."}}