{"id":"W6957569651","doi":"10.6068/dp14ba8a7e4c686","title":"Trend 1999 - 2009. 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: Purchases, Non-chain stores, Used and recreational motor vehicle and parts dealers | Units: , 1999-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-177.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Retail trade; Census; Summary statistics; Economic statistics; Descriptive statistics; Retail sales; Year-ending; Financial services; Index (typography)","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":[0.001490184,0.002679141,0.002574881,0.00818925,0.002246163,0.004388334,0.005066323,0.001254615,0.06685152],"category_scores_gemma":[0.01232435,0.001575856,0.001842919,0.04054558,0.0005635067,0.002428955,0.001895078,0.002742998,0.06517175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02945336,"about_ca_system_score_gemma":0.07170124,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9878512,"about_ca_topic_score_gemma":0.9846511,"domain_scores_codex":[0.9965072,0.0001812983,0.0003671091,0.0005114567,0.001639376,0.0007936203],"domain_scores_gemma":[0.9746451,0.0007824848,0.0009409185,0.0007365103,0.02188324,0.001011816],"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":[0.00002750789,0.000007204736,0.001236503,0.0001921841,0.0000195075,0.000007395151,0.00001252401,0.0001120758,0.000009664966,0.0002129312,0.9969642,0.001198389],"study_design_scores_gemma":[0.0002152462,0.0000180772,0.03822996,0.0008121663,0.00006571157,0.00003309683,0.0004413432,0.0006920087,0.0002463812,0.0005194093,0.9586389,0.00008777864],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005403822,0.00002694895,0.00001229869,0.00005113889,0.00001578505,0.000007622373,0.9993223,0.00003712318,0.0004727913],"genre_scores_gemma":[0.0004521626,0.0001173157,0.0001327782,0.0000550821,0.000009800422,0.00004313942,0.9970478,0.00004684732,0.002094976],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06685152,"threshold_uncertainty_score":0.2236406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121715815095537,"score_gpt":0.2213936090848494,"score_spread":0.190176450933894,"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."}}