{"id":"W2032091434","doi":"10.1287/opre.1100.0906","title":"Adaptive Data-Driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator","year":2011,"lang":"en","type":"article","venue":"Operations Research","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":196,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Office of Naval Research; National Science Foundation","keywords":"Estimator; Newsvendor model; Computer science; Nonparametric statistics; Consistency (knowledge bases); Mathematical optimization; Econometrics; Inventory control; Quantile; Product (mathematics); Mathematics; Statistics; Operations research","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":[0.006527887,0.0008774624,0.001898521,0.0009646918,0.0003643,0.001289174,0.001973389,0.001104159,0.001538541],"category_scores_gemma":[0.02019422,0.0006518162,0.0008668855,0.0008988827,0.001582627,0.001979026,0.001335817,0.002064714,0.0002760566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195526,"about_ca_system_score_gemma":0.001479493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003062447,"about_ca_topic_score_gemma":0.001602146,"domain_scores_codex":[0.9978051,0.001062489,0.0001167409,0.0004031967,0.0004569532,0.00015563],"domain_scores_gemma":[0.9870047,0.009781703,0.001134251,0.0007672503,0.001096476,0.0002156182],"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":[0.00009142322,0.00004752265,0.001205136,0.00007996555,0.00008212274,0.00005055755,0.00009132615,0.9247921,0.001141705,0.04690508,0.0004882345,0.02502485],"study_design_scores_gemma":[0.00001424466,0.0000258201,0.0001230037,0.00000596305,0.000007815001,0.00001045419,0.000004220471,0.9895991,0.0002696651,0.009727813,0.0002006911,0.00001115427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009042104,0.0001599933,0.9901805,0.00008154482,0.00001489485,0.00002484407,0.00002550065,0.0001287898,0.0003418113],"genre_scores_gemma":[0.8015152,0.0006108119,0.1951288,0.0001354561,0.0000858526,0.0002859399,0.0001970241,0.0000796634,0.001961147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006527887,"threshold_uncertainty_score":0.03452319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1650692678379945,"score_gpt":0.3201718275990081,"score_spread":0.1551025597610136,"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."}}