{"id":"W2539727260","doi":"","title":"How to calculate forecast accuracy for stocked items with a lumpy demand : A case study at Alfa Laval","year":2016,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Demand forecasting; Stock (firearms); Profit (economics); Operations research; Inventory management; Economics; Profit maximization; Computer science; Econometrics; Actuarial science; Operations management; Microeconomics; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00177836,0.0003755424,0.0005567445,0.001310886,0.0005976966,0.0002886996,0.001863293,0.0002197303,0.00005860856],"category_scores_gemma":[0.008025952,0.0002333454,0.0001272852,0.00251371,0.0005554847,0.001102603,0.0011739,0.0001830396,0.00004192171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001556264,"about_ca_system_score_gemma":0.0001988613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004483176,"about_ca_topic_score_gemma":0.002178107,"domain_scores_codex":[0.9962183,0.00005586741,0.0009781534,0.001323277,0.0008555362,0.0005689195],"domain_scores_gemma":[0.9936048,0.0004581962,0.0008904116,0.002906456,0.001851576,0.0002885876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009567679,0.003104174,0.06488681,0.0001018096,0.0004161415,0.0003843864,0.0007689063,0.0002111537,0.01391159,0.08715413,0.3053472,0.5227569],"study_design_scores_gemma":[0.003780298,0.001602876,0.001411482,0.000185435,0.0001544825,0.0008117073,0.001279623,0.005338225,0.01629854,0.002764324,0.9654486,0.0009244254],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7431377,0.00002668446,0.2262939,0.0245574,0.0001137612,0.003575109,0.001588676,0.0005019146,0.0002048972],"genre_scores_gemma":[0.9274855,0.00000483219,0.06716421,0.0001247141,0.00006074624,0.002414723,0.0001605112,0.00003716472,0.002547643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6601014,"threshold_uncertainty_score":0.9608394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1045922589797975,"score_gpt":0.3710720804000557,"score_spread":0.2664798214202582,"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."}}