{"id":"W4413318893","doi":"10.1109/access.2025.3600468","title":"Domain Adaptation for Retail Demand Prediction","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Institut de Valorisation des Données","keywords":"Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); On demand; Artificial intelligence; Multimedia; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001903037,0.0007226834,0.0008484328,0.001462198,0.0003000239,0.0006562249,0.0008691562,0.000624963,0.001239882],"category_scores_gemma":[0.005335917,0.0002718994,0.0007659325,0.001877239,0.0002665208,0.00100687,0.0006265903,0.001356928,0.0008109431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005351189,"about_ca_system_score_gemma":0.0006833161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00815545,"about_ca_topic_score_gemma":0.005268046,"domain_scores_codex":[0.999335,0.00025893,0.000046965,0.000185679,0.0001046625,0.00006868033],"domain_scores_gemma":[0.997996,0.001094239,0.0001436626,0.0002963188,0.0004119888,0.0000578385],"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":[0.000293619,0.0005023679,0.01782931,0.0001534168,0.0002139107,0.0001389736,0.000140895,0.5464202,0.003608814,0.00240813,0.01120362,0.4170867],"study_design_scores_gemma":[0.000005580757,0.00002274738,0.001403502,0.000005739209,0.000007259247,0.00001710394,0.00002786639,0.995327,0.0005806349,0.00165768,0.0009380078,0.000006875422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1665741,0.002111284,0.8194134,0.0007035875,0.0003851184,0.000246405,0.002101686,0.004324672,0.00413988],"genre_scores_gemma":[0.8856389,0.0007895521,0.1069751,0.0002498827,0.0001432322,0.0002049748,0.003547421,0.0001071674,0.002343672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00815545,"threshold_uncertainty_score":0.01621598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04538142596367648,"score_gpt":0.3251650828672476,"score_spread":0.2797836569035711,"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."}}