{"id":"W4320061486","doi":"10.1109/etcea57049.2022.10009861","title":"Business Intelligence System for Human Resource Management System","year":2022,"lang":"en","type":"article","venue":"","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Resource management (computing); Human resource management system; Human resource management; Business intelligence; Knowledge management; Process management; Business; Distributed computing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00242537,0.001411847,0.001315405,0.003817594,0.001090881,0.006405356,0.002350478,0.001604198,0.0829747],"category_scores_gemma":[0.006489259,0.0005134492,0.0006995965,0.003385084,0.0003132668,0.003409159,0.002492934,0.002070352,0.0932197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127646,"about_ca_system_score_gemma":0.002270973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002848246,"about_ca_topic_score_gemma":0.001335743,"domain_scores_codex":[0.9977805,0.0003316654,0.0003344385,0.0005583861,0.0007824631,0.0002125545],"domain_scores_gemma":[0.9970564,0.0004661258,0.0002800423,0.0007121553,0.00120022,0.0002851006],"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":[0.0005249322,0.0002581439,0.0033099,0.0006983185,0.00009677377,0.0004031683,0.0002969259,0.001197074,0.003253068,0.01737437,0.7572976,0.2152897],"study_design_scores_gemma":[0.0001881075,0.00007978641,0.003150224,0.0001817893,0.00006675699,0.0002757356,0.0001204712,0.01880312,0.006201399,0.01152942,0.9593254,0.0000779062],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01151246,0.004225242,0.166992,0.00889095,0.002877656,0.004291755,0.1310077,0.3123699,0.3578323],"genre_scores_gemma":[0.2015361,0.005420721,0.2283996,0.00983337,0.002102168,0.006454573,0.3179668,0.01015153,0.2181351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0829747,"threshold_uncertainty_score":0.2775781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06452176603356691,"score_gpt":0.2750169762256573,"score_spread":0.2104952101920904,"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."}}