{"id":"W4385577341","doi":"10.1109/accai58221.2023.10201125","title":"Artificial Intelligence enabled Employee Performance Prediction using Comprehensive Learning Metrics","year":2023,"lang":"en","type":"article","venue":"","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Human capital; Machine learning; Recall; Knowledge management; Artificial intelligence; Trait; Precision and recall; Data science; Psychology; Cognitive psychology","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":[0.002389173,0.0008623613,0.0008179425,0.003059019,0.0003462321,0.001646242,0.0006213908,0.0008327155,0.0008829621],"category_scores_gemma":[0.005608108,0.0001445511,0.0005639201,0.002207176,0.0002214286,0.001614333,0.0007387674,0.000807741,0.0004633587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007044315,"about_ca_system_score_gemma":0.0006590516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005788328,"about_ca_topic_score_gemma":0.005929894,"domain_scores_codex":[0.9992119,0.0002216267,0.00007247525,0.0001699867,0.0002276008,0.00009637555],"domain_scores_gemma":[0.9970279,0.001520175,0.0003637386,0.0002679752,0.0006857424,0.0001344236],"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":[0.0002483564,0.000572001,0.1076483,0.00012462,0.0002534414,0.0001260533,0.0001287611,0.4891355,0.001895383,0.00288889,0.004747363,0.3922313],"study_design_scores_gemma":[0.000002169138,0.00008023157,0.012077,0.000018328,0.00002465469,0.0000271316,0.00002100646,0.9851681,0.0008386318,0.001193319,0.0005350893,0.00001428151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7445683,0.003085494,0.2388253,0.001044189,0.0001339061,0.0001338935,0.001870947,0.001802035,0.008535803],"genre_scores_gemma":[0.978964,0.0002915995,0.01848903,0.00003749545,0.00004541074,0.00003572044,0.001097726,0.0000136883,0.001025413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005788328,"threshold_uncertainty_score":0.01263535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101597383738803,"score_gpt":0.2732218611052542,"score_spread":0.1716244773664511,"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."}}