{"id":"W4392175700","doi":"10.1109/upcon59197.2023.10434761","title":"Machine Learning and Human Resource Management: A Path to Efficient Workforce Management","year":2023,"lang":"en","type":"article","venue":"","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Human resource management; Resource management (computing); Workforce; Computer science; Path (computing); Knowledge management; Artificial intelligence; Distributed computing; Computer network","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":[0.007975399,0.0005215396,0.0006756667,0.001215764,0.001304965,0.006019143,0.001011157,0.002670842,0.006297399],"category_scores_gemma":[0.01549767,0.0002615833,0.0002742164,0.002954961,0.005018975,0.01148145,0.002269801,0.003833645,0.0009052356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003258131,"about_ca_system_score_gemma":0.006739449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003633302,"about_ca_topic_score_gemma":0.003078109,"domain_scores_codex":[0.9944898,0.003358173,0.0001302692,0.0003323471,0.001246377,0.0004430227],"domain_scores_gemma":[0.9852584,0.01110904,0.00121001,0.0005826735,0.001137918,0.000702052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006933646,0.000285901,0.00909795,0.0007269414,0.00004777871,0.0000764021,0.001061305,0.01062033,0.0002250333,0.6829231,0.02397017,0.2708957],"study_design_scores_gemma":[0.00003153318,0.0001241714,0.006850412,0.001726201,0.00001402834,0.00007406365,0.002909092,0.02474257,0.0005965078,0.8462338,0.1166328,0.000064858],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0586112,0.06436479,0.2337624,0.4596869,0.001719163,0.0003122803,0.0004135789,0.000273159,0.1808566],"genre_scores_gemma":[0.8412662,0.03393094,0.09805741,0.01420095,0.002210557,0.0003257805,0.0002304266,0.00008753048,0.009690188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007975399,"threshold_uncertainty_score":0.04217845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562182330693459,"score_gpt":0.2360025743081086,"score_spread":0.220380751001174,"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."}}