{"id":"W7140310498","doi":"10.1109/gcwcn66157.2025.11448508","title":"Enhancing Recruitment Efficiency in HRM through Intelligent Resume Screening and Job Matching Using Fuzzy Logic and Ensemble Learning","year":2025,"lang":"","type":"article","venue":"","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Matching (statistics); Fuzzy logic; Ensemble learning; Fuzzy control system; Human resource management; Job evaluation","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001470065,0.0006162873,0.0007577833,0.001017898,0.001203206,0.001145052,0.0003594465,0.0003731132,0.00008143397],"category_scores_gemma":[0.0004793138,0.0005915197,0.0001002718,0.001493482,0.0002794822,0.001566179,0.00209009,0.0009519656,0.00001979083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001601373,"about_ca_system_score_gemma":0.00005395855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003216346,"about_ca_topic_score_gemma":0.0007833175,"domain_scores_codex":[0.9963926,0.00004983716,0.001085285,0.001132895,0.0003276401,0.001011719],"domain_scores_gemma":[0.9988149,0.000263695,0.000416379,0.0003536462,0.000127169,0.00002422305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004159408,0.0004676565,0.1478045,0.005385261,0.0003674284,0.0002366374,0.005986114,0.01348684,0.0310145,0.4609998,0.0002231229,0.3336121],"study_design_scores_gemma":[0.006728356,0.000469862,0.02622317,0.02524167,0.001354601,0.00006305407,0.2366356,0.2199077,0.02200122,0.4216343,0.03388681,0.005853685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7784413,0.01262466,0.1774094,0.001485962,0.0003941301,0.00113437,4.792783e-7,0.000349728,0.02816006],"genre_scores_gemma":[0.9803196,0.002804825,0.01413113,0.0009313833,0.0001367892,0.00002917561,0.000003632914,0.00004241696,0.001601087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3277585,"threshold_uncertainty_score":0.9998919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07554486966452431,"score_gpt":0.312870160208618,"score_spread":0.2373252905440937,"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."}}