{"id":"W6901579462","doi":"10.6068/dp14ba8f65fd450","title":"Trend 2000 - 2004. Statistics Canada. CANSIM: Information and Communications Technology - Business and Government Internet Use | Country: Canada | Table: Survey of electronic commerce and technology, barriers to electronic commerce, by North American Industry Classification System (NAICS) | Variable: Users/non-users of Internet who do not use electronic commerce, Petroleum product wholesaler-distributors, Uncertain about benefits | Units: %, 2000-2004. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-125.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"The Internet; Business statistics; Government (linguistics); Economic statistics; Official statistics; Information and Communications Technology; Information technology; Summary statistics; Census","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":[0.001938063,0.002477173,0.002752657,0.01071108,0.003509924,0.004684363,0.005425415,0.001565826,0.06438908],"category_scores_gemma":[0.01860471,0.001718343,0.002189674,0.04843322,0.0006704384,0.002579263,0.002415562,0.003193479,0.03785283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05653403,"about_ca_system_score_gemma":0.1496384,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948891,"about_ca_topic_score_gemma":0.9932356,"domain_scores_codex":[0.995351,0.0002481146,0.0005069073,0.0005053909,0.002309385,0.001079252],"domain_scores_gemma":[0.9587131,0.001303641,0.001405684,0.0009034279,0.03586777,0.001806412],"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":[0.00002437364,0.000008961714,0.001496082,0.0002745216,0.00002253963,0.000006983772,0.00002385269,0.0001154331,0.000007980419,0.000393266,0.9961547,0.001471296],"study_design_scores_gemma":[0.0001786787,0.00001997443,0.04477425,0.0009912607,0.0001004776,0.00003201581,0.000807205,0.0006489604,0.0002331039,0.0006473854,0.9514648,0.0001018651],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007856556,0.00004906511,0.00001976424,0.0001225796,0.00002723007,0.00001509322,0.9989183,0.0000432899,0.0007261008],"genre_scores_gemma":[0.001003574,0.0002734313,0.0002698569,0.0001349056,0.00001909584,0.0001121014,0.9940906,0.00006287433,0.00403352],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06438908,"threshold_uncertainty_score":0.410185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848646257059133,"score_gpt":0.2359085863376775,"score_spread":0.2174221237670861,"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."}}