{"id":"W4283790057","doi":"10.26389/ajsrp.m211221","title":"A Bibliometric Analysis of Artificial Intelligence applications during COVID-19 Based on Web of Science (WoS) Database","year":2022,"lang":"en","type":"article","venue":"مجلة العلوم الهندسية و تكنولوجيا المعلومات","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bibliometrics; Coronavirus disease 2019 (COVID-19); Web of science; China; Data science; Field (mathematics); Geography; Regional science; Computer science; Political science; Library science; MEDLINE; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.003107464,0.0004079418,0.00109579,0.1457571,0.0009667908,0.00005955682,0.001274534,0.00009883361,0.002200963],"category_scores_gemma":[0.004171514,0.0004354726,0.0005425078,0.3561168,0.001398396,0.0002296875,0.0006998992,0.0005952065,0.00003719301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001266141,"about_ca_system_score_gemma":0.004172522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007531897,"about_ca_topic_score_gemma":0.00007554801,"domain_scores_codex":[0.9932747,0.0002445425,0.001378232,0.001382813,0.002967215,0.0007525329],"domain_scores_gemma":[0.9933602,0.001936121,0.0008318108,0.002491336,0.0006278873,0.000752653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003477906,0.01328416,0.1076781,0.003107161,0.001761859,0.0003118174,0.001815563,0.4100895,0.4044736,0.01186081,0.003864435,0.0382751],"study_design_scores_gemma":[0.003810246,0.002794967,0.1476634,0.0004317856,0.008364072,0.00007648723,0.002786638,0.5524624,0.219943,0.0007304233,0.05875259,0.002183985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9396522,0.0005790842,0.04253298,0.01145797,0.0004016354,0.002608004,0.001679528,0.0003740675,0.0007145168],"genre_scores_gemma":[0.9913539,0.00007985296,0.002331548,0.005234307,0.00009188,0.0005508993,0.0002267104,0.00005574885,0.00007519361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2103598,"threshold_uncertainty_score":0.9998097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08751307564465516,"score_gpt":0.402218653709887,"score_spread":0.3147055780652319,"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."}}