{"id":"W2069007600","doi":"10.1108/00197850010345791","title":"Intelligent training motivates the HR and payroll teams","year":2000,"lang":"en","type":"article","venue":"Industrial and Commercial Training","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Payroll; Training (meteorology); Investment (military); Business; Public relations; Management; Psychology; Marketing; Operations management; Economics; Accounting; Political science; Law; Politics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004014808,0.000192967,0.0002476251,0.00008961472,0.0005736316,0.0003656085,0.0002280434,0.0001733336,0.0002526582],"category_scores_gemma":[0.0001333088,0.0001315609,0.00004804088,0.000260352,0.0002640017,0.0003649109,0.0001252688,0.0004195186,0.00002270702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008641391,"about_ca_system_score_gemma":0.00001879535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003894933,"about_ca_topic_score_gemma":0.00006595546,"domain_scores_codex":[0.9990269,0.000015359,0.0002493456,0.0002371417,0.0001380452,0.0003331915],"domain_scores_gemma":[0.9996212,0.0001307052,0.00008047379,0.0001293578,0.00002113451,0.00001718009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004686962,0.00001290583,0.003137978,0.000004359458,0.00002058152,0.000003722183,0.001686596,0.000009684166,0.00002177636,0.005729223,0.0009499443,0.9883764],"study_design_scores_gemma":[0.002050157,0.0001009165,0.01481682,0.0001871827,0.0001330964,0.00001784571,0.04908863,0.0008896777,0.00008469186,0.02030274,0.911646,0.000682264],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967866,0.0001990484,0.00002716164,0.007272648,0.0002341792,0.0002118788,0.000002710816,0.0002148331,0.02397155],"genre_scores_gemma":[0.9956874,0.00009556829,0.00002917722,0.001696173,0.00218934,0.00001381346,0.000005566774,0.00001733123,0.0002656449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9876941,"threshold_uncertainty_score":0.5364899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1307351385506157,"score_gpt":0.2479042936535197,"score_spread":0.1171691551029039,"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."}}