{"id":"W6920689180","doi":"10.6068/dp14ba8e9a85958","title":"Trend 2002 - 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, Other information services, Cost of development and maintenance is too high | Units: %, 2002-2006. 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; Government (linguistics); Official statistics; Economic statistics; Census; Telephone number; Information technology; 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":[0.001981908,0.002561449,0.002814626,0.01004397,0.003414698,0.004755008,0.005274469,0.001576725,0.06390704],"category_scores_gemma":[0.01802538,0.001642813,0.002234674,0.04665075,0.0006254525,0.002470951,0.002399226,0.003270076,0.04135834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04899184,"about_ca_system_score_gemma":0.1388535,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936816,"about_ca_topic_score_gemma":0.9923051,"domain_scores_codex":[0.9953963,0.000250077,0.0005083977,0.000521509,0.002310367,0.001013416],"domain_scores_gemma":[0.9588442,0.00122054,0.001291479,0.0009080747,0.03604082,0.001694823],"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":[0.00002153356,0.000008245297,0.001342971,0.0002664056,0.00002133082,0.00000624826,0.0000199016,0.0001012626,0.000007399655,0.000296286,0.9966072,0.001301238],"study_design_scores_gemma":[0.0001779827,0.00001821728,0.04299489,0.001089763,0.00009586406,0.00003127341,0.0007365046,0.0005923769,0.0002114662,0.0005843224,0.9533675,0.00009994205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006317401,0.00004592094,0.00001705372,0.0001141197,0.00002753424,0.00001392462,0.9990678,0.00003812342,0.0006123803],"genre_scores_gemma":[0.0007897905,0.0002391064,0.0002279205,0.0001275623,0.00001880958,0.0001028329,0.9952887,0.00005611439,0.003149109],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06390704,"threshold_uncertainty_score":0.3554623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169268905342754,"score_gpt":0.2191302712334739,"score_spread":0.2022033806991985,"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."}}