{"id":"W6901581160","doi":"10.6068/dp14ba8d298c727","title":"Trend 2000 - 2006. 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, Machinery manufacturing, Security concerns | Units: %, 2000-2006. 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; Information and Communications Technology; Census; Information technology; Summary statistics","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":[0.001988902,0.002416179,0.0026977,0.009637789,0.003564865,0.004745152,0.005307272,0.001509703,0.06774306],"category_scores_gemma":[0.01801863,0.001648026,0.002169701,0.04425934,0.0006367289,0.002590105,0.002467077,0.003301145,0.04378381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04733281,"about_ca_system_score_gemma":0.1346247,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933594,"about_ca_topic_score_gemma":0.9920322,"domain_scores_codex":[0.9957397,0.0002458113,0.0004544661,0.0004973657,0.00207984,0.0009827714],"domain_scores_gemma":[0.9615473,0.001208783,0.001246102,0.0009302461,0.03334322,0.001724249],"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":[0.00002197399,0.000008083022,0.001347608,0.0002383375,0.00001976549,0.000006303709,0.00002302209,0.00009686746,0.000007456442,0.0003221457,0.9965486,0.001359873],"study_design_scores_gemma":[0.0001760212,0.00001852251,0.03991805,0.001027713,0.00008929607,0.00003185501,0.0007855552,0.0005815671,0.0002104574,0.0006795587,0.9563823,0.00009914044],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006623335,0.00004005698,0.00001962591,0.0001132292,0.0000261772,0.00001473195,0.9990349,0.00004354145,0.0006416062],"genre_scores_gemma":[0.0007346343,0.0002164239,0.000263595,0.0001293999,0.00001866853,0.0001122453,0.9952124,0.00006626262,0.003246184],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06774306,"threshold_uncertainty_score":0.3434252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01821080154142417,"score_gpt":0.2414768237025516,"score_spread":0.2232660221611274,"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."}}