{"id":"W4398182762","doi":"10.1287/mnsc.2022.01108","title":"Self-Adapting Network Relaxations for Weakly Coupled Markov Decision Processes","year":2024,"lang":"en","type":"article","venue":"Management Science","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov decision process; Computer science; Markov chain; Statistical physics; Markov process; Mathematical optimization; Econometrics; Mathematics; Machine learning; Physics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006474392,0.00008126545,0.00006275472,0.0001104289,0.0002382426,0.0002606609,0.000218854,0.00002051775,0.00001155475],"category_scores_gemma":[0.0001140634,0.00007354803,0.0000231397,0.001338675,0.00004729873,0.0004799007,0.00005659041,0.00004579683,0.0000212932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111749,"about_ca_system_score_gemma":0.00002647159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001001024,"about_ca_topic_score_gemma":0.000005715825,"domain_scores_codex":[0.9990939,0.000002917943,0.000156387,0.0002594995,0.000214795,0.0002724215],"domain_scores_gemma":[0.9996048,0.0001100157,0.00001548533,0.000164843,0.00006751365,0.00003738309],"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":[0.000003601952,0.000009616447,0.0000295512,0.0007414912,0.00001212529,0.000001631886,0.0001315422,0.9564022,0.00005082185,0.01900456,0.004345027,0.0192678],"study_design_scores_gemma":[0.0000569882,0.00001279571,0.0001616593,0.0001793445,0.00001714664,6.11095e-7,0.00006001088,0.9597903,0.00003913659,0.003691312,0.03589562,0.00009508032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002921982,0.0003401608,0.9798086,0.0001682932,0.0009009815,0.0005736037,0.000001361495,0.0007365041,0.01454853],"genre_scores_gemma":[0.5911967,0.0007438913,0.4069631,0.00004617893,0.000105018,0.0001858932,0.000005238429,0.00002180827,0.0007322733],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5882747,"threshold_uncertainty_score":0.2999201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00595439663458187,"score_gpt":0.224123714524245,"score_spread":0.2181693178896631,"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."}}