{"id":"W4415974692","doi":"10.1016/j.procs.2025.09.181","title":"Improving Nurse Scheduling Using a Random Forest Algorithm to Predict Employee Well-Being","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Aluminium Refining, Degassing and Filtering (Canada); Group for Research in Decision Analysis; Université du Québec à Chicoutimi","funders":"Mitacs","keywords":"Random forest; Scheduling (production processes); Overtime; Work (physics); Linear programming; Job shop scheduling","routes":{"ca_aff":true,"ca_fund":true,"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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006997615,0.0002924513,0.0004539314,0.001715499,0.001479147,0.001872807,0.00258481,0.00009149467,0.00001980257],"category_scores_gemma":[0.005200876,0.0002437732,0.0001653907,0.007684752,0.0004795774,0.001180749,0.0008124297,0.0003231246,0.0002327695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001713996,"about_ca_system_score_gemma":0.001215994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008343344,"about_ca_topic_score_gemma":0.0000117693,"domain_scores_codex":[0.9945509,0.00009359843,0.0008705489,0.001513243,0.001939964,0.001031679],"domain_scores_gemma":[0.9960796,0.001058676,0.0002241746,0.0009524066,0.001235115,0.0004500565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008048383,0.0001916748,0.09605406,0.00003951104,0.00004590397,0.00002429504,0.002932067,0.3364204,0.004460819,0.0043457,0.0007903943,0.5546147],"study_design_scores_gemma":[0.0007276444,0.00005343614,0.005022783,0.0001516098,0.00002843394,0.00002207423,0.0001245031,0.9830609,0.001424557,0.008780968,0.0003329914,0.000270076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2458024,0.00008361707,0.7499257,0.0004338506,0.002535668,0.0003707776,0.000002260645,0.0001866329,0.0006590062],"genre_scores_gemma":[0.500701,0.000001041581,0.4981944,0.0005674097,0.0003232984,0.00001947066,5.129542e-7,0.000009931216,0.0001829888],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6466405,"threshold_uncertainty_score":0.9998208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03274423289670193,"score_gpt":0.344962055914178,"score_spread":0.3122178230174761,"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."}}