{"id":"W2766331864","doi":"10.1016/j.ijpe.2017.10.024","title":"Coping with risk management and fill rate in the loss-averse newsvendor model","year":2017,"lang":"en","type":"article","venue":"International Journal of Production Economics","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Science Foundation of Shandong Province; Hong Kong Polytechnic University; China Postdoctoral Science Foundation; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Newsvendor model; CVAR; Loss aversion; Economics; Expected shortfall; Economic shortage; Risk aversion (psychology); Economic order quantity; Microeconomics; Econometrics; Risk management; Actuarial science; Expected utility hypothesis; Supply chain; Business; Mathematical economics","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":[0.0008190811,0.00008900286,0.0001015719,0.0002291169,0.0001538455,0.0005255968,0.00048195,0.00001541192,0.0000265097],"category_scores_gemma":[0.00006250513,0.00006548245,0.00003890358,0.00002522507,0.00007022224,0.001647766,0.0001196195,0.0001172794,0.00001226593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000644918,"about_ca_system_score_gemma":0.00001002799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005567624,"about_ca_topic_score_gemma":0.00009688253,"domain_scores_codex":[0.9993628,0.00001017054,0.000270875,0.0001433117,0.0001199291,0.0000929572],"domain_scores_gemma":[0.9989073,0.000009952461,0.0007572148,0.000194845,0.0001235183,0.000007206053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001416302,0.0005435787,0.2837904,0.0001981486,0.001202819,0.0003084507,0.001248129,0.3804458,0.0000250742,0.2186496,0.05039579,0.06177597],"study_design_scores_gemma":[0.006696164,0.00007511079,0.1613829,0.0002864937,0.0002974827,0.0001546236,0.003296015,0.1167786,0.000106568,0.07091737,0.6393601,0.0006485356],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679531,0.00003660145,0.001377331,0.01808602,0.002019163,0.0002276916,0.000002375069,0.000007848455,0.01028985],"genre_scores_gemma":[0.9954459,0.0005769556,0.0005603664,0.001499655,0.001402906,0.000005680005,0.000002658345,0.00001012659,0.0004957646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5889643,"threshold_uncertainty_score":0.5068342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02315741727506924,"score_gpt":0.2325639134488688,"score_spread":0.2094064961737996,"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."}}