{"id":"W6901318805","doi":"10.6068/dp14ba8ddc6885","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, Accommodation and food services, 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":"Business statistics; The Internet; Government (linguistics); Official statistics; Economic statistics; Information technology; Census; Information and Communications Technology; Summary statistics; Descriptive 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":[0.002125005,0.002543393,0.002787832,0.009993726,0.003499676,0.004815574,0.005451987,0.001529796,0.06902052],"category_scores_gemma":[0.01908412,0.001690516,0.002225946,0.04748521,0.0006495694,0.002653971,0.002469249,0.003407803,0.04590341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04914883,"about_ca_system_score_gemma":0.1384146,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935769,"about_ca_topic_score_gemma":0.9919086,"domain_scores_codex":[0.9954802,0.0002604659,0.000500904,0.0005236424,0.002226094,0.001008684],"domain_scores_gemma":[0.9594343,0.001295373,0.001285305,0.000969337,0.03526257,0.001753155],"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":[0.00002101614,0.000007916878,0.001195864,0.0002462009,0.00001923215,0.000005701324,0.00002139485,0.00009321537,0.000006958241,0.0002900624,0.9967626,0.001329801],"study_design_scores_gemma":[0.0001726097,0.00001752535,0.03754378,0.001026555,0.0000887858,0.00002912942,0.0007376286,0.0005599562,0.0002021807,0.0006270833,0.9588974,0.00009735129],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005893732,0.00004058904,0.00001793646,0.0001068408,0.00002661077,0.00001380146,0.9990892,0.00004282138,0.0006032223],"genre_scores_gemma":[0.0006634307,0.0002130308,0.0002433412,0.0001184539,0.00001813397,0.0001088499,0.9954661,0.00006411869,0.003104431],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06902052,"threshold_uncertainty_score":0.3566014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01942871792324292,"score_gpt":0.240086072653449,"score_spread":0.2206573547302061,"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."}}