{"id":"W4206667539","doi":"10.1007/978-3-030-92270-2_13","title":"An Implicit Learning Approach for Solving the Nurse Scheduling Problem","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Regina","funders":"","keywords":"Computer science; Nurse scheduling problem; Scheduling (production processes); Job shop scheduling; Artificial intelligence; Operations research; Mathematical optimization; Flow shop scheduling; Schedule; Mathematics; Operating system","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":[0.0009812724,0.0005744699,0.0007716641,0.0003251629,0.0003834177,0.0007210431,0.00228299,0.001035014,0.004573712],"category_scores_gemma":[0.004078884,0.0004451715,0.0004717543,0.0007356444,0.0007181505,0.001522778,0.001463176,0.002202388,0.0005010568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007707627,"about_ca_system_score_gemma":0.001505343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005891301,"about_ca_topic_score_gemma":0.007288339,"domain_scores_codex":[0.9995531,0.0001688892,0.00002046033,0.00006711398,0.0001333733,0.00005700432],"domain_scores_gemma":[0.9987602,0.000899994,0.00006985894,0.00006852538,0.0001607625,0.00004067409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001178021,0.000141942,0.0002991156,0.0001313874,0.00002962165,0.00003877603,0.00008830935,0.7788014,0.001057381,0.06869801,0.00233776,0.1482584],"study_design_scores_gemma":[0.000008914722,0.00001368683,0.00003074657,0.000004982645,0.000003792238,0.000004229744,0.000004267055,0.984447,0.0001354587,0.01486062,0.0004838997,0.000002422331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006758424,0.0001827985,0.9894373,0.0001740177,0.00003903465,0.00001997467,0.00002201677,0.00009883837,0.003267577],"genre_scores_gemma":[0.3603147,0.0006333992,0.6198045,0.0002230386,0.0002195245,0.0002763776,0.0001944022,0.0001123643,0.01822168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005891301,"threshold_uncertainty_score":0.01530057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06718189093934655,"score_gpt":0.3475020556820189,"score_spread":0.2803201647426724,"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."}}