{"id":"W2153410625","doi":"10.1111/1468-0394.00140","title":"Developing a neural network approach for intelligent scheduling in GUESS","year":2000,"lang":"en","type":"article","venue":"Expert Systems","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Maryland Industrial Partnerships","keywords":"Computer science; Scheduling (production processes); Artificial neural network; Dynamic priority scheduling; Fair-share scheduling; Two-level scheduling; Artificial intelligence; Machine learning; Mathematical optimization; Schedule; 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":[0.0007011911,0.0004366814,0.0004150423,0.0004864863,0.0003567954,0.0007667614,0.0008482824,0.0008062484,0.00432359],"category_scores_gemma":[0.001854693,0.0004434243,0.0004658583,0.0004682745,0.000472112,0.001385374,0.0005655909,0.001053435,0.0006649626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009351755,"about_ca_system_score_gemma":0.001238466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01023708,"about_ca_topic_score_gemma":0.01252623,"domain_scores_codex":[0.9997764,0.00007193114,0.0000174367,0.0000473876,0.00006267237,0.0000241285],"domain_scores_gemma":[0.9995331,0.000265508,0.00002698624,0.00003724133,0.0001238726,0.00001341045],"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":[0.00003991255,0.00003092046,0.0003484216,0.000059636,0.00002417494,0.00002494776,0.00006777781,0.9069087,0.002039001,0.01828825,0.0008494211,0.07131884],"study_design_scores_gemma":[0.000004300865,0.000008369469,0.00004383332,0.000004275477,0.00000375039,0.000004243767,0.00000704905,0.9941046,0.0007473174,0.004326476,0.0007427431,0.000003087495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008753002,0.00009606001,0.9857201,0.0001433809,0.00002229973,0.00006136008,0.00004185264,0.0006510003,0.00451093],"genre_scores_gemma":[0.2458259,0.0002691956,0.7467241,0.0001232714,0.00002894158,0.0002819357,0.00016089,0.0001301454,0.006455597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01023708,"threshold_uncertainty_score":0.02035499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05510028095815973,"score_gpt":0.2865697383176091,"score_spread":0.2314694573594494,"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."}}