{"id":"W6976662523","doi":"10.6068/dp14ba8e3ade420","title":"Trend 2002 - 2005. 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, Prefer to maintain current business model | Units: %, 2002-2005. 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; 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":[0.002149942,0.002504285,0.002770383,0.01019814,0.003520259,0.004912951,0.005454604,0.001520386,0.06953289],"category_scores_gemma":[0.0199061,0.001680892,0.002217798,0.04725866,0.0006249841,0.002620516,0.002519441,0.003215034,0.04374289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0526187,"about_ca_system_score_gemma":0.1436071,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940901,"about_ca_topic_score_gemma":0.9924655,"domain_scores_codex":[0.9951911,0.0002625402,0.0005424259,0.0005273456,0.002414911,0.001061662],"domain_scores_gemma":[0.9545968,0.001368434,0.001372799,0.001000799,0.03979663,0.001864422],"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":[0.00002228892,0.000008395166,0.001355538,0.0002546918,0.00002083662,0.000006196746,0.00002191237,0.00009393039,0.000006985491,0.0003024901,0.9964771,0.001429716],"study_design_scores_gemma":[0.0001809308,0.00001937362,0.04408937,0.001138161,0.0000976612,0.00002989585,0.0008217159,0.0005947888,0.0002185212,0.0006182743,0.9520885,0.0001027817],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006835432,0.00004453694,0.00001892526,0.0001188022,0.00002886236,0.00001599533,0.9989927,0.00004397003,0.000667823],"genre_scores_gemma":[0.0008206166,0.0002326369,0.0002492999,0.0001342312,0.00001916023,0.0001173871,0.9947534,0.00006322023,0.003610135],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06953289,"threshold_uncertainty_score":0.3817772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.022390423823362,"score_gpt":0.2373687699618932,"score_spread":0.2149783461385312,"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."}}