{"id":"W4399171888","doi":"10.1287/msom.2022.0453","title":"Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach","year":2024,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pooling; Computer science; Exploit; Boosting (machine learning); Gradient boosting; Leverage (statistics); Scalability; Econometrics; Machine learning; Artificial intelligence; Random forest; 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":[0.001674605,0.0001464273,0.0001476399,0.0003925246,0.0004327082,0.0009136987,0.0002517043,0.00005509274,0.00004109466],"category_scores_gemma":[0.00004539382,0.0001246862,0.00004882678,0.0004480893,0.00001822401,0.0003744873,0.0001246788,0.0001857742,0.00001957203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005403804,"about_ca_system_score_gemma":0.00001133721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007506226,"about_ca_topic_score_gemma":0.000140351,"domain_scores_codex":[0.998325,0.00005415622,0.0004816689,0.0006012068,0.0003108148,0.0002270958],"domain_scores_gemma":[0.9994247,0.0001867605,0.00002001182,0.0002620486,0.00005757749,0.00004892957],"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":[0.00002177153,0.00008410244,0.0004890651,0.000539309,0.00005772721,0.000006973141,0.003461195,0.8260136,0.0004147762,0.02921066,0.0005522445,0.1391485],"study_design_scores_gemma":[0.0002174222,0.00003374869,0.003012465,0.0001449359,0.00004011152,0.000008601137,0.001235701,0.9635787,0.0008463697,0.003862045,0.02685619,0.0001637126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4075058,0.0001407065,0.5835237,0.001717896,0.00007713168,0.001107445,0.00001348991,0.0002980953,0.005615711],"genre_scores_gemma":[0.9610376,0.00003115696,0.03575209,0.0001587135,0.00006294229,0.0005433478,0.000039268,0.00002309555,0.002351722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5535318,"threshold_uncertainty_score":0.8810818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07303055777460699,"score_gpt":0.3259016177324117,"score_spread":0.2528710599578047,"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."}}