{"id":"W4387874151","doi":"10.1007/s44163-023-00084-2","title":"Artificial intelligence in Africa: a bibliometric analysis from 2013 to 2022","year":2023,"lang":"en","type":"article","venue":"Discover Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Scopus; Context (archaeology); Productivity; Library science; Political science; Bibliometrics; Regional science; Geography; Economic growth; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004622396,0.0004163067,0.0008623624,0.09063126,0.001029796,0.005294655,0.0005440834,0.0005680443,0.002963128],"category_scores_gemma":[0.03007125,0.000189341,0.0007242327,0.1528465,0.0005934233,0.003781689,0.001883964,0.0004868657,0.0007174194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002506667,"about_ca_system_score_gemma":0.004347475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01130318,"about_ca_topic_score_gemma":0.009110193,"domain_scores_codex":[0.9954874,0.0007173163,0.000935721,0.0004282084,0.001958993,0.0004723318],"domain_scores_gemma":[0.9737619,0.009305205,0.008625479,0.0005258331,0.006931851,0.0008498287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003215532,0.0001177059,0.6450207,0.01060006,0.0008784817,0.001733746,0.00729238,0.002279699,0.00114159,0.01076972,0.03846683,0.2813775],"study_design_scores_gemma":[0.00001976006,0.00007757262,0.8217922,0.00518157,0.0005164157,0.001515661,0.01582621,0.002079721,0.0009969412,0.002101358,0.1498246,0.000067922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7853896,0.06967972,0.001516386,0.006792845,0.0003614849,0.0002695103,0.1000177,0.0001829072,0.03578982],"genre_scores_gemma":[0.9335397,0.03741239,0.001685453,0.0002719603,0.0003325546,0.0002000912,0.02480181,0.0000493853,0.001706715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9093688,"threshold_uncertainty_score":0.02444589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2384266171390101,"score_gpt":0.4378532182722857,"score_spread":0.1994266011332756,"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."}}