{"id":"W2055508815","doi":"10.1016/j.apm.2011.12.005","title":"Inventory models for imperfect quality items with shortages and learning in inspection","year":2011,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Imperfect; Quality (philosophy); Vendor; Economic shortage; Economic order quantity; Computer science; Production (economics); Task (project management); Order (exchange); Operations research; Operations management; Risk analysis (engineering); Marketing; Business; Supply chain; Economics; Microeconomics; Engineering","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":[0.005330076,0.001305012,0.002782402,0.001681979,0.0006769639,0.003348155,0.004709009,0.003636403,0.006539922],"category_scores_gemma":[0.020297,0.001876895,0.001920277,0.002612635,0.003198249,0.005190429,0.001759212,0.002819989,0.0006941093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005143924,"about_ca_system_score_gemma":0.002461584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03392085,"about_ca_topic_score_gemma":0.01836854,"domain_scores_codex":[0.9982643,0.0006268769,0.0001378489,0.0002883637,0.0002836677,0.0003990045],"domain_scores_gemma":[0.9868107,0.009289042,0.001767107,0.0004163075,0.001302138,0.0004147385],"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":[0.0000501668,0.00004218486,0.0006128055,0.00006547278,0.000015147,0.00009056242,0.00008620043,0.9668034,0.0001230585,0.0296202,0.0004751371,0.00201566],"study_design_scores_gemma":[0.00001165957,0.00001803747,0.0002811908,0.00001169704,0.00001191439,0.0000212098,0.00002352097,0.9829873,0.00004069322,0.01642173,0.000156591,0.00001438444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2304103,0.002737843,0.7345045,0.003954734,0.0002793177,0.0001809604,0.0009858833,0.0005076841,0.02643873],"genre_scores_gemma":[0.9495409,0.001021932,0.01880028,0.000168597,0.0001060447,0.0001217006,0.0003562473,0.00009054352,0.02979375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03392085,"threshold_uncertainty_score":0.06744683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08826526748179904,"score_gpt":0.243428133472458,"score_spread":0.1551628659906589,"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."}}