{"id":"W6939362991","doi":"10.6068/dp14ba8cc7a3276","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 the Internet that do not sell, Accommodation and food services, 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":"Business statistics; The Internet; Government (linguistics); Official statistics; Economic statistics; Census; Information technology; Information and Communications Technology; Telephone number; 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":[0.002068295,0.002577888,0.002758201,0.009925242,0.003416539,0.004799051,0.005401871,0.001540828,0.06791351],"category_scores_gemma":[0.01885343,0.001662621,0.002177583,0.04756299,0.0006622185,0.002638525,0.00244434,0.003362246,0.046236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04656801,"about_ca_system_score_gemma":0.1314825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.993161,"about_ca_topic_score_gemma":0.9914662,"domain_scores_codex":[0.9955869,0.0002577275,0.0004907553,0.0005276842,0.002151063,0.0009859924],"domain_scores_gemma":[0.9614038,0.001298928,0.001246363,0.0009690304,0.0334145,0.001667301],"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":[0.00002030945,0.000007719958,0.001132836,0.0002420851,0.00001900219,0.00000563217,0.00002069202,0.00009592432,0.000007043503,0.0002827847,0.9969019,0.001264246],"study_design_scores_gemma":[0.0001716648,0.00001668715,0.03421175,0.0009778374,0.00008532554,0.00002744848,0.0007042909,0.000550484,0.0001978331,0.0006242371,0.9623374,0.00009489612],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005406009,0.00003773372,0.00001675627,0.00009796872,0.00002427619,0.00001217959,0.9991786,0.00004148359,0.0005368522],"genre_scores_gemma":[0.0006100744,0.0001917673,0.0002262985,0.0001057097,0.00001640684,0.00009746046,0.9959725,0.00006114137,0.002718722],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06791351,"threshold_uncertainty_score":0.3378761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01995595221155994,"score_gpt":0.2299062821371706,"score_spread":0.2099503299256106,"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."}}