{"id":"W6901420471","doi":"10.6068/dp14ba8e2563883","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, Information and cultural industries, 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 technology; Information and Communications Technology; Census; 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":[0.0020962,0.002524572,0.002750238,0.01004348,0.003556572,0.004814361,0.005466998,0.001524022,0.06774774],"category_scores_gemma":[0.0190543,0.00168179,0.00218227,0.04721679,0.0006521487,0.002656697,0.002482664,0.003380125,0.04510969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04961875,"about_ca_system_score_gemma":0.140021,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937342,"about_ca_topic_score_gemma":0.9921879,"domain_scores_codex":[0.995577,0.0002557664,0.0004819075,0.0005173071,0.00217418,0.0009939588],"domain_scores_gemma":[0.9590093,0.001283613,0.001269388,0.0009677095,0.03568269,0.001787196],"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":[0.00002114463,0.000008052278,0.001242488,0.0002400029,0.00001931814,0.000005699198,0.00002190814,0.00009411881,0.000006931059,0.0002958547,0.9967092,0.001335273],"study_design_scores_gemma":[0.0001753729,0.00001786451,0.03892217,0.001013643,0.0000902391,0.00002968649,0.0007723559,0.0005747514,0.0002058602,0.000646768,0.9574525,0.00009871519],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006270342,0.00004046928,0.00001842134,0.0001088405,0.00002682668,0.00001431055,0.9990696,0.00004358983,0.0006151849],"genre_scores_gemma":[0.0006815859,0.0002077048,0.000243239,0.0001178143,0.00001810097,0.0001094451,0.9954703,0.00006317235,0.003088623],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06774774,"threshold_uncertainty_score":0.3600109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02150179185815129,"score_gpt":0.2433963206063158,"score_spread":0.2218945287481645,"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."}}