{"id":"W6901843553","doi":"10.6068/dp14ba8ede39511","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, Pipeline transportation, Prefer to maintain current business model | 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; Information technology; Information and Communications Technology; Census; Telephone number","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":[0.002099818,0.002462692,0.002678758,0.009980912,0.003531724,0.004643444,0.005358473,0.001510942,0.0687855],"category_scores_gemma":[0.01967197,0.00166034,0.002195048,0.04566094,0.0006261763,0.002609134,0.002502359,0.003217645,0.04372865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04935884,"about_ca_system_score_gemma":0.1372229,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936773,"about_ca_topic_score_gemma":0.9921864,"domain_scores_codex":[0.9955564,0.0002451813,0.0005036693,0.0005066787,0.002199281,0.000988788],"domain_scores_gemma":[0.9568121,0.001352162,0.001361458,0.0009760216,0.03769292,0.001805411],"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":[0.00002288742,0.000008725817,0.001451531,0.0002574805,0.00001981735,0.000006141687,0.00002295579,0.00009598395,0.000007401028,0.0003042528,0.9963474,0.00145548],"study_design_scores_gemma":[0.0001880466,0.00001997869,0.0464242,0.001118824,0.00009527682,0.00003142666,0.0008154944,0.0006174165,0.0002260537,0.0006499022,0.9497076,0.0001058413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006695974,0.00003979377,0.00001850201,0.0001071364,0.00002611079,0.00001569012,0.9990446,0.00004256508,0.0006386525],"genre_scores_gemma":[0.0007546386,0.0002183353,0.0002485758,0.000122122,0.00001804671,0.0001161606,0.9952117,0.00006050308,0.003250028],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0687855,"threshold_uncertainty_score":0.3581252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.021805085520246,"score_gpt":0.2446059427328301,"score_spread":0.2228008572125841,"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."}}