{"id":"W6939018672","doi":"10.6068/dp14ba8f8004626","title":"Trend 2000 - 2004. 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, Concern about competitors analyzing company information | Units: %, 2000-2004. 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; Competitor analysis; Government (linguistics); Official statistics; Business statistics; Economic statistics; Information technology; Census; Information and Communications 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":[0.002186214,0.00251342,0.002728796,0.01053049,0.003606578,0.004827511,0.005661237,0.001547851,0.06808391],"category_scores_gemma":[0.02025695,0.001745233,0.002198816,0.04682185,0.0006492655,0.002631453,0.002503591,0.003237332,0.04304752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05326952,"about_ca_system_score_gemma":0.1437686,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9938877,"about_ca_topic_score_gemma":0.9923078,"domain_scores_codex":[0.9952785,0.0002602086,0.0005213264,0.0005355043,0.002354788,0.001049694],"domain_scores_gemma":[0.9535783,0.001442096,0.001438621,0.001041618,0.04063745,0.001861828],"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":[0.00002318124,0.000008986866,0.001474213,0.000247181,0.00002022901,0.000006320276,0.00002292733,0.00009695528,0.000007693753,0.0003000447,0.9963321,0.001460266],"study_design_scores_gemma":[0.0001840815,0.00001932629,0.0461362,0.001044938,0.00009649582,0.00002957149,0.0008078971,0.0006045595,0.0002269031,0.0006212528,0.9501276,0.000101088],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007199607,0.00004020222,0.00001931508,0.0001089158,0.00002713458,0.00001692734,0.9989986,0.00004547248,0.0006715355],"genre_scores_gemma":[0.0007627165,0.0002094187,0.0002540191,0.000121411,0.00001831133,0.0001248035,0.9949366,0.00006362002,0.003509006],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06808391,"threshold_uncertainty_score":0.3864992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736572709865871,"score_gpt":0.2372813264469518,"score_spread":0.2199155993482931,"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."}}