{"id":"W4393036678","doi":"10.2139/ssrn.4761142","title":"Clustering-Based Demand Forecasting with an Application to Immunoglobulin Products","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; McMaster University","funders":"","keywords":"Cluster analysis; Demand forecasting; Computer science; Data science; Econometrics; Economics; Artificial intelligence; Operations management","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":[0.0009222262,0.0007300135,0.001088895,0.001530746,0.0006796761,0.0008393252,0.00116712,0.001489673,0.001924564],"category_scores_gemma":[0.004294724,0.0004379036,0.0008602828,0.003003793,0.0002790587,0.0007485072,0.0004576142,0.0008735258,0.0004039711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009123334,"about_ca_system_score_gemma":0.000757565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02937336,"about_ca_topic_score_gemma":0.01655674,"domain_scores_codex":[0.9997481,0.0000773795,0.00001603077,0.00006750532,0.00005884281,0.00003210684],"domain_scores_gemma":[0.9980949,0.001263478,0.00009688333,0.0001552021,0.0003248596,0.00006476887],"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":[0.000164216,0.0001002425,0.002043035,0.00007080838,0.00005365611,0.0001110185,0.00006896687,0.8960654,0.001855064,0.00480496,0.002041913,0.09262082],"study_design_scores_gemma":[0.000001856918,0.00000491348,0.0002074236,0.000001287225,0.000002505528,0.000005616285,0.000004195178,0.9985704,0.0001733017,0.0009333435,0.00009179716,0.000003355986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1926546,0.0007360476,0.7998291,0.0005526117,0.0001737362,0.000118332,0.0008033237,0.00216396,0.002968308],"genre_scores_gemma":[0.7991621,0.0004129859,0.1964916,0.00006071551,0.0001141374,0.00008193347,0.0007203788,0.0001617646,0.002794492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02937336,"threshold_uncertainty_score":0.0584048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05691485609492793,"score_gpt":0.3744313207864516,"score_spread":0.3175164646915237,"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."}}