{"id":"W2119459533","doi":"10.1109/tsm.2003.822725","title":"An Optimal Residency-Aware Scheduling Technique for Cluster Tools With Buffer Module","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scheduling (production processes); Semiconductor device fabrication; Computer science; Time constraint; Distributed computing; Resource constraints; Job shop scheduling; Cluster (spacecraft); Throughput; Resource (disambiguation); Buffer (optical fiber); Wafer; Real-time computing; Mathematical optimization; Computer network; Engineering; Operating system; Mathematics; Routing (electronic design automation)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000142016,0.0003689539,0.0002719791,0.0002635054,0.0002567299,0.0001605963,0.0002411117,0.0002254748,0.00008828054],"category_scores_gemma":[0.000003606784,0.0003519062,0.0001138048,0.0001323244,0.00004362899,0.0007855648,0.000001156423,0.0004641076,0.00001778298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002309252,"about_ca_system_score_gemma":0.00006598815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001367844,"about_ca_topic_score_gemma":0.00002460729,"domain_scores_codex":[0.9985517,0.00001796528,0.0003210921,0.0004447638,0.0002180957,0.0004463477],"domain_scores_gemma":[0.9991537,0.00008835556,0.00004414408,0.0004612889,0.00007251672,0.0001799694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009133655,0.00005503764,9.533678e-7,0.00006672908,0.00005889051,0.000003198793,0.0002322484,0.9732743,0.02412398,0.00000553335,0.000004137945,0.002083659],"study_design_scores_gemma":[0.00120157,0.0001678121,0.00001096985,0.0001760925,0.00004874167,0.00004533009,0.0002124773,0.07731926,0.920232,0.00008476775,0.00002739033,0.0004736241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3581615,0.00001809701,0.6402109,0.00003564858,0.0003763582,0.0005110895,0.00004649318,0.0006134361,0.00002646773],"genre_scores_gemma":[0.6793488,0.00001529203,0.3200267,0.00006250395,0.000107997,0.0002826944,0.00001395423,0.0001067894,0.00003535932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.896108,"threshold_uncertainty_score":0.9998933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197078827702216,"score_gpt":0.2438777265884775,"score_spread":0.2241698438182559,"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."}}