{"id":"W6976516216","doi":"10.6068/dp14ba8361f8495","title":"Trend 1999 - 2004. Statistics Canada. CANSIM: Retail and Wholesale - Retail Sales by Type of Store | Country: Canada | Table: Annual retail store survey, financial estimates by store type and trade group based on the North American Industry Classification System (NAICS) | Variable: Total revenue, Total all stores, Clothing stores | Units: , 1999-2004. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-177.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Impulse Buying and Technology Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Retail trade; Census; Economic statistics; Summary statistics; Retail sales; Official statistics; Descriptive statistics; Index (typography); Financial services","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00114214,0.0007277891,0.00131951,0.0001668358,0.0002447641,0.0001690059,0.001174601,0.0008190819,0.00013411],"category_scores_gemma":[0.0008076765,0.0007093827,6.364621e-7,0.0006350784,0.0006247426,0.0002295045,0.0002409286,0.001604372,0.000006142421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005484667,"about_ca_system_score_gemma":0.004284244,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953201,"about_ca_topic_score_gemma":0.9764429,"domain_scores_codex":[0.9962521,0.000342505,0.00100867,0.001215306,0.0004408005,0.0007406616],"domain_scores_gemma":[0.9951153,0.0008324954,0.001592874,0.001986657,0.00008566782,0.0003870152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001759982,0.00007081623,0.003258181,0.0003702159,0.0002264338,0.00006989914,0.00001039776,0.00003869861,0.000001342962,0.0004631314,0.9950997,0.0002152237],"study_design_scores_gemma":[0.0005750977,0.000317394,0.0008961894,0.00008391237,0.0001749237,0.00006044818,0.0003010674,0.00499622,3.516296e-8,2.10358e-7,0.9918612,0.0007333013],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004045935,0.009655763,0.000008240936,0.00002426789,0.000600372,0.0004825105,0.9885424,0.00007238897,0.0002094282],"genre_scores_gemma":[0.006671758,0.0003956518,0.00008487771,0.0001977209,0.0001159527,0.00001261524,0.9913463,0.0002014833,0.0009736608],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01887723,"threshold_uncertainty_score":0.9995357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04160522735392621,"score_gpt":0.2293709053840179,"score_spread":0.1877656780300916,"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."}}