{"id":"W6938580473","doi":"10.6068/dp14ba8ec5a9c43","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, Transit and ground passenger transportation, Suppliers not ready | 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; Official statistics; Government (linguistics); Economic statistics; Census; Information and Communications Technology; Telephone number; Information technology","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":[0.002046702,0.002477192,0.002682272,0.009801789,0.00350373,0.004647434,0.005266375,0.00149428,0.06566762],"category_scores_gemma":[0.01894867,0.0016405,0.002127766,0.04558881,0.0006301365,0.002523958,0.002431605,0.003221453,0.04200244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0484647,"about_ca_system_score_gemma":0.1374193,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939303,"about_ca_topic_score_gemma":0.9924794,"domain_scores_codex":[0.9956321,0.0002510798,0.000490335,0.0005160221,0.002134349,0.0009762112],"domain_scores_gemma":[0.9601591,0.00128237,0.001247827,0.0009234815,0.03470467,0.001682508],"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":[0.00002219556,0.000008223446,0.00136418,0.0002549729,0.00002017177,0.000006295965,0.00002269588,0.0001009199,0.000007460476,0.0003112841,0.9964641,0.001417632],"study_design_scores_gemma":[0.0001795026,0.00001863226,0.04174435,0.001072587,0.00009501606,0.00003093269,0.0007700923,0.0006173786,0.0002144369,0.0006450742,0.9545112,0.0001008483],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006402705,0.00004194942,0.00001858568,0.0001060183,0.00002607744,0.00001405284,0.999092,0.00004168669,0.0005956305],"genre_scores_gemma":[0.0007405527,0.0002228112,0.0002484747,0.0001185377,0.00001756548,0.0001045038,0.9954438,0.00006054392,0.003043219],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06566762,"threshold_uncertainty_score":0.3516377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01807942654399962,"score_gpt":0.2315971131669477,"score_spread":0.213517686622948,"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."}}