{"id":"W3159071455","doi":"10.1002/nav.21994","title":"Dynamically scheduling and maintaining a flexible server","year":2021,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Computer science; Markov decision process; Markov chain; Mathematical optimization; Semiconductor device fabrication; Schedule; Fair-share scheduling; Job shop scheduling; Dynamic priority scheduling; Operations research; Distributed computing; Real-time computing; Markov process; Mathematics; Engineering; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001227076,0.0001187114,0.0001666514,0.0001022651,0.0001627725,0.000169972,0.0001417728,0.0001474316,0.00007653849],"category_scores_gemma":[0.00318197,0.00012134,0.00003596266,0.0004483579,0.000201929,0.0001163051,0.0001789153,0.0006480679,0.00004437063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001916071,"about_ca_system_score_gemma":0.0001369344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003144473,"about_ca_topic_score_gemma":0.0000504927,"domain_scores_codex":[0.9983935,0.00010671,0.0002166266,0.0002851384,0.0004416559,0.0005563605],"domain_scores_gemma":[0.9986061,0.0004049561,0.00001331184,0.0002958644,0.0005139344,0.0001657649],"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":[0.00005289201,0.00006377287,0.000876684,0.0004303707,0.00005515708,0.000194625,0.0002901544,0.8828396,0.009350461,0.09416729,0.0005422573,0.0111367],"study_design_scores_gemma":[0.0003027574,0.00006721301,0.0007583108,0.0001304728,0.000008966592,0.00001982203,0.0004371238,0.9672517,0.002844667,0.02594354,0.002035244,0.0002001802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2135472,0.001523667,0.7429184,0.001004614,0.0004431119,0.0003985473,0.00002972976,0.0005129121,0.03962186],"genre_scores_gemma":[0.9651712,0.0009380326,0.03293886,0.00003778586,0.0000906927,0.00001687019,0.00003256793,0.00003318266,0.0007408328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.751624,"threshold_uncertainty_score":0.49481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06027633868240124,"score_gpt":0.3365222179903641,"score_spread":0.2762458793079628,"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."}}