{"id":"W6976490932","doi":"10.6068/dp14ba8e1550f76","title":"Trend 2002 - 2005. 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 of Internet who do not use electronic commerce, Other information services, Lack of skilled employees | Units: %, 2002-2005. 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); Official statistics; Economic statistics; Census; Information technology; Information and Communications Technology; Telephone number","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":[0.002218139,0.002530496,0.002858489,0.01001029,0.003484267,0.004875959,0.005629627,0.001530558,0.06776528],"category_scores_gemma":[0.01935718,0.00176828,0.002232517,0.04675326,0.0006175119,0.00257644,0.002467288,0.003346686,0.04310551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05204679,"about_ca_system_score_gemma":0.1455471,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941601,"about_ca_topic_score_gemma":0.9923753,"domain_scores_codex":[0.9951447,0.000277189,0.0005536255,0.0005411389,0.002414316,0.00106898],"domain_scores_gemma":[0.9561588,0.001302615,0.001291147,0.0009711122,0.03845049,0.001825782],"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":[0.00002268646,0.000008567125,0.00131973,0.0002545724,0.00002129142,0.000006102816,0.00002136454,0.00009293432,0.000006956878,0.0002818002,0.9965588,0.001405052],"study_design_scores_gemma":[0.0001932551,0.00002013918,0.04381869,0.001160439,0.0001026689,0.00003160814,0.0008170484,0.0006117353,0.0002153272,0.0006269299,0.9523004,0.000101861],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006738939,0.00004534655,0.00001942188,0.0001175932,0.00003025674,0.00001635597,0.9990236,0.00004375725,0.0006363437],"genre_scores_gemma":[0.0007879763,0.0002355256,0.0002548792,0.0001372357,0.00001985489,0.0001202227,0.9948201,0.00006545377,0.003558752],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06776528,"threshold_uncertainty_score":0.3776277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205230601167676,"score_gpt":0.2452729291892798,"score_spread":0.2247498690725122,"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."}}