{"id":"W1965111581","doi":"10.1016/j.apm.2012.07.046","title":"An entropic economic order quantity (EnEOQ) for items with imperfect quality","year":2012,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Università degli Studi di Brescia","keywords":"Economic order quantity; Imperfect; Entropy (arrow of time); Mathematical optimization; Computer science; Mathematics; Econometrics; Thermodynamics; Supply chain; Physics","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":[0.003585869,0.0008794122,0.001390613,0.002046618,0.0008722608,0.002893564,0.002334224,0.002321522,0.006791473],"category_scores_gemma":[0.0171239,0.0008445615,0.001446616,0.002201914,0.003337553,0.007347648,0.003007973,0.001963069,0.0004235618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002551623,"about_ca_system_score_gemma":0.002086759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004188922,"about_ca_topic_score_gemma":0.002747456,"domain_scores_codex":[0.9984427,0.000580353,0.0001078746,0.0002207352,0.0004682863,0.0001801352],"domain_scores_gemma":[0.9932811,0.003488936,0.001029405,0.0007132448,0.001018991,0.0004682819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004914302,0.00003371008,0.0006464729,0.0001272665,0.00002514729,0.0001284898,0.00007443743,0.2580375,0.0007389912,0.732943,0.001160199,0.006035588],"study_design_scores_gemma":[0.000008882266,0.00002398077,0.0004097547,0.00002794371,0.00001065089,0.00005796695,0.00002176455,0.7852368,0.0001661717,0.2127465,0.001262845,0.00002667804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07715613,0.001519102,0.8907533,0.001786045,0.000522407,0.0001275767,0.0004564927,0.0001635306,0.02751541],"genre_scores_gemma":[0.8938527,0.001299596,0.07806876,0.0002846056,0.0004296314,0.0001371997,0.0002648862,0.0002343445,0.02542834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006791473,"threshold_uncertainty_score":0.02271974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05348982923869217,"score_gpt":0.2755254641611309,"score_spread":0.2220356349224387,"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."}}