{"id":"W4382118239","doi":"10.2139/ssrn.4490516","title":"Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Pooling; Transfer of learning; Gradient boosting; Computer science; On demand; Artificial intelligence; Machine learning; Business; Commerce","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":[0.00969163,0.00008343665,0.0001410855,0.0003086596,0.0003859659,0.0001424352,0.0002065177,0.00006531824,0.000004640201],"category_scores_gemma":[0.0008447816,0.00006699411,0.00006414403,0.0006886653,0.00003088167,0.0001739608,0.00002830269,0.0009221982,0.000005138708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001221066,"about_ca_system_score_gemma":0.0002248538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001107618,"about_ca_topic_score_gemma":0.00006257658,"domain_scores_codex":[0.9979893,0.00007799343,0.0004097324,0.0002596655,0.0003076791,0.0009556089],"domain_scores_gemma":[0.9993338,0.0003614606,0.00007699835,0.0000961979,0.00008422368,0.00004731667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002112323,0.0001039788,0.0747411,0.00003306816,0.00008163834,0.000003672019,0.003295637,0.04381739,0.004860051,0.2724836,0.000940363,0.5994283],"study_design_scores_gemma":[0.0005909039,0.0002575786,0.003293632,0.00003429285,0.00001567967,0.0002650281,0.003428024,0.387483,0.00008965754,0.6014188,0.002990618,0.0001327566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5681601,0.0002519596,0.4303548,0.0004139557,0.00002309772,0.000196585,0.000002054439,0.00008629881,0.0005111601],"genre_scores_gemma":[0.9964303,0.0003913573,0.001612591,0.00001304034,0.0001032665,0.0000404795,0.000004392371,0.00001440862,0.001390124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5992955,"threshold_uncertainty_score":0.4006543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08124982622709631,"score_gpt":0.3435170331532377,"score_spread":0.2622672069261414,"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."}}