{"id":"W6901576661","doi":"10.6068/dp14ba8f5b0c174","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/non-users of Internet who do not use electronic commerce, Health care and social assistance public, Uncertain about benefits | 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; Government (linguistics); Official statistics; Business statistics; Economic statistics; Information and Communications Technology; Telephone number; Information technology; Census","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":[0.002185394,0.002485692,0.002879092,0.01003968,0.003404879,0.004617308,0.005584846,0.001620161,0.06629829],"category_scores_gemma":[0.02008958,0.001713609,0.002365351,0.04463028,0.0006483207,0.002478362,0.002427922,0.003358956,0.03851447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05446672,"about_ca_system_score_gemma":0.1508269,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9945311,"about_ca_topic_score_gemma":0.9927906,"domain_scores_codex":[0.9953886,0.0002725395,0.000534922,0.000508668,0.002259168,0.001036084],"domain_scores_gemma":[0.9594471,0.001336369,0.001363322,0.0009157984,0.0351452,0.001792278],"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":[0.00002508844,0.00000862862,0.001417386,0.0002840586,0.00002349489,0.00000658515,0.00002186768,0.0001072166,0.000007038628,0.0003351263,0.9963259,0.001437635],"study_design_scores_gemma":[0.0002159683,0.0000211809,0.04602091,0.001173445,0.0001150556,0.00003361992,0.0007735905,0.0006519447,0.0002159653,0.0006711236,0.9499995,0.0001077726],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007359382,0.00005147878,0.00001938542,0.0001315829,0.00003011649,0.00001612046,0.9989564,0.00004355904,0.000677681],"genre_scores_gemma":[0.0009326697,0.0002744438,0.0002816312,0.000154774,0.00002180061,0.0001298782,0.9942029,0.00006477278,0.003937096],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06629829,"threshold_uncertainty_score":0.3951856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02288033567315082,"score_gpt":0.2520350030222986,"score_spread":0.2291546673491478,"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."}}