{"id":"W2087380820","doi":"10.3182/20060517-3-fr-2903.00004","title":"PROCESS DESIGN FOR EFFICIENT SCHEDULING","year":2006,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scheduling (production processes); Architecture; Bridging (networking); Computer science; Process management; Manufacturing engineering; Industrial engineering; Engineering; Operations management; Risk analysis (engineering); Systems engineering; Business","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":[0.0007853119,0.0008358255,0.0006734658,0.0005531128,0.0006149975,0.001245395,0.001101301,0.0006775134,0.007235788],"category_scores_gemma":[0.001351994,0.0006238897,0.000713794,0.0007219039,0.0004250321,0.001018837,0.0006867645,0.001078519,0.001772356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009362847,"about_ca_system_score_gemma":0.002125775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266619,"about_ca_topic_score_gemma":0.002078394,"domain_scores_codex":[0.9992518,0.0001466645,0.00003099229,0.0001556869,0.0003238097,0.00009114442],"domain_scores_gemma":[0.9996096,0.000102748,0.00004640917,0.00009033665,0.0001369823,0.00001392735],"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":[0.000183607,0.0001917669,0.0003769067,0.0003971729,0.00004972705,0.0001124318,0.0001005217,0.6016309,0.05856385,0.1325216,0.004278346,0.2015932],"study_design_scores_gemma":[0.00004873782,0.0001566034,0.0001510528,0.00002221176,0.00002941362,0.00005136651,0.00002524572,0.9261684,0.01967351,0.03735028,0.01631019,0.00001301778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005940728,0.0002194343,0.9841323,0.0001153659,0.0000548916,0.0001036306,0.00005961656,0.0003732748,0.009000763],"genre_scores_gemma":[0.3214709,0.0007643053,0.6621509,0.0001682416,0.00007704493,0.0004329893,0.0003868309,0.0002743701,0.01427432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007235788,"threshold_uncertainty_score":0.02420616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075365563584008,"score_gpt":0.2155061227261403,"score_spread":0.2047524670903002,"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."}}