{"id":"W4405442077","doi":"10.3390/app142411750","title":"From Recruitment to Retention: AI Tools for Human Resource Decision-Making","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West","funders":"","keywords":"Onboarding; Computer science; Analytics; Documentation; Personalization; Knowledge management; Data science; Psychology; World Wide Web","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004887084,0.0001088747,0.0001169926,0.0002492541,0.0005794127,0.001816279,0.0005088237,0.0000533712,0.0001437876],"category_scores_gemma":[0.0001076892,0.00008455633,0.00005400462,0.0007181704,0.0001093558,0.0006137391,0.0002668869,0.00007036507,0.0002689075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002303164,"about_ca_system_score_gemma":0.00001248382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002825462,"about_ca_topic_score_gemma":0.00003596539,"domain_scores_codex":[0.9988442,0.000001339034,0.0001825271,0.000460303,0.0002711126,0.0002404995],"domain_scores_gemma":[0.9995189,0.0002222608,0.00004498071,0.0001803509,0.00002635103,0.000007149856],"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":[0.00001201411,0.00001091956,0.00006875627,0.00002572452,0.00000894689,0.000002671738,0.00009272229,0.00007203367,0.002312897,0.5049065,0.05489283,0.437594],"study_design_scores_gemma":[0.00006213081,0.00001568083,0.0002719419,0.000154107,0.00001751066,1.62491e-7,0.001707771,0.00061082,0.0002881968,0.3824447,0.614275,0.000151984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7320515,0.0005772532,0.06813148,0.01583568,0.001375575,0.002675987,0.00001785175,0.002132555,0.1772021],"genre_scores_gemma":[0.9903003,0.000001176223,0.006160741,0.002291636,0.0008339436,0.0002455922,0.000008595463,0.00001029811,0.0001477189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5593822,"threshold_uncertainty_score":0.99922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1219909297000671,"score_gpt":0.3495166410801659,"score_spread":0.2275257113800988,"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."}}