{"id":"W2905605495","doi":"10.2139/ssrn.3194640","title":"Promotion Optimization in Retail","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Business; Promotion (chess); Marketing; Advertising; Political science; Law","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":[0.001235555,0.0008924141,0.002852739,0.00121776,0.0007412374,0.002747037,0.001110259,0.001702828,0.0128693],"category_scores_gemma":[0.004337491,0.0009212695,0.0009825446,0.001347729,0.001125448,0.002265677,0.001108968,0.001339321,0.000597065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830207,"about_ca_system_score_gemma":0.001799911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009067831,"about_ca_topic_score_gemma":0.007242354,"domain_scores_codex":[0.9992757,0.0002872557,0.00002420515,0.0001484333,0.00007332152,0.0001909871],"domain_scores_gemma":[0.9984517,0.0009823305,0.0001332412,0.00009328824,0.0001566682,0.0001828663],"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":[0.0007011162,0.0007643104,0.003270379,0.0002435721,0.0001489934,0.0001567402,0.0001626544,0.8253188,0.001619899,0.08954129,0.01011465,0.06795756],"study_design_scores_gemma":[0.00003755788,0.000140564,0.0009807508,0.00002198201,0.00005637219,0.00003060422,0.0001113944,0.9571857,0.0003513272,0.03972581,0.001338798,0.00001914626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5378571,0.004138991,0.3338768,0.003878026,0.0003864222,0.0003700062,0.0006577275,0.001044492,0.1177904],"genre_scores_gemma":[0.9657835,0.0004261617,0.01411928,0.00009728115,0.00005623477,0.00005252926,0.0001378143,0.00007786707,0.01924943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0128693,"threshold_uncertainty_score":0.04305208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619687517217987,"score_gpt":0.2335211184349921,"score_spread":0.2173242432628122,"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."}}