{"id":"W2904637935","doi":"10.1002/9781119522225.ch6","title":"Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales","year":2018,"lang":"en","type":"other","venue":"","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Receipt; Sample (material); Quarter (Canadian coin); Computer science; Big data; Estimation; Data science; World Wide Web; Data mining; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002905613,0.00078396,0.0003493993,0.00262879,0.00069204,0.004391365,0.0008636144,0.001107443,0.02655131],"category_scores_gemma":[0.01847986,0.0003176041,0.000425857,0.003202372,0.0004913547,0.0055543,0.001810993,0.001243022,0.01514757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008001068,"about_ca_system_score_gemma":0.001152084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01235934,"about_ca_topic_score_gemma":0.02373697,"domain_scores_codex":[0.9990501,0.0003940094,0.00004796452,0.0001482057,0.0003115346,0.00004811163],"domain_scores_gemma":[0.9924498,0.004129463,0.0003696381,0.001316222,0.00125186,0.0004831693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001928599,0.0002964528,0.06126461,0.0001594034,0.00008165212,0.0001070153,0.0005165644,0.003429521,0.0006353621,0.01690272,0.4029664,0.5134475],"study_design_scores_gemma":[0.0000639475,0.0001431082,0.0961665,0.0005434963,0.000147014,0.0002371707,0.001869938,0.08882485,0.003484654,0.1507356,0.6576052,0.000178607],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1582222,0.01262123,0.1066087,0.08583526,0.004752864,0.0003689213,0.1035762,0.03453756,0.493477],"genre_scores_gemma":[0.6121308,0.007745385,0.08495659,0.00571076,0.003150388,0.0002863968,0.0625942,0.005582331,0.2178431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02655131,"threshold_uncertainty_score":0.08882296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06237458649236343,"score_gpt":0.2592857143623429,"score_spread":0.1969111278699795,"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."}}