{"id":"W2102704639","doi":"10.1109/tsm.2007.914388","title":"A Multiagent-Based Decision-Making System for Semiconductor Wafer Fabrication With Hard Temporal Constraints","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Workcell; Wafer fabrication; Job shop scheduling; Computer science; Scheduling (production processes); Semiconductor device fabrication; Schedule; Distributed computing; Mathematical optimization; Engineering; Artificial intelligence; Wafer; Robot; Mathematics","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":[0.001219628,0.0006530082,0.0007665145,0.0002883133,0.001109294,0.001371247,0.001514662,0.0009895434,0.003676472],"category_scores_gemma":[0.001496162,0.0004067305,0.0006959776,0.0003249571,0.0006138954,0.0009768244,0.001056697,0.001136435,0.0004630584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009277408,"about_ca_system_score_gemma":0.001697658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004417256,"about_ca_topic_score_gemma":0.00437701,"domain_scores_codex":[0.9993419,0.0001840797,0.00005548029,0.0001762659,0.0001693129,0.00007290758],"domain_scores_gemma":[0.9993274,0.0003217035,0.00009270727,0.00005175352,0.0001160873,0.00009043267],"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":[0.0003172913,0.0003086966,0.001107516,0.000235219,0.000140155,0.0008273222,0.0003046891,0.8498338,0.0136163,0.04303595,0.002943511,0.08732966],"study_design_scores_gemma":[0.00005020088,0.00006194625,0.00009622096,0.000007447504,0.00002219016,0.00003216667,0.00001884173,0.9917099,0.001423737,0.004267732,0.002296483,0.00001311718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02225863,0.0002025919,0.9727868,0.0003058157,0.0000924282,0.0001748091,0.00006573484,0.0006728134,0.003440461],"genre_scores_gemma":[0.6100182,0.0002338094,0.3845101,0.0001522556,0.00007731048,0.0004911738,0.0001559317,0.0000430905,0.004318082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004417256,"threshold_uncertainty_score":0.01229906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399545512018482,"score_gpt":0.2331846611622516,"score_spread":0.2091892060420668,"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."}}