{"id":"W6957703112","doi":"10.6068/dp14ba8f1f3f938","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 the Internet that do not buy, Accommodation and food services, Goods do not lend themselves to Internet transactions | 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; Government (linguistics); Official statistics; Economic statistics; Telephone number; Information and Communications Technology; Information technology; Descriptive statistics","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":[0.001935301,0.002462842,0.002686301,0.01005178,0.003422496,0.004546852,0.005151961,0.001505288,0.05856698],"category_scores_gemma":[0.01733672,0.001583488,0.002154128,0.04602701,0.000637269,0.002477815,0.00239369,0.003242217,0.03942917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04742983,"about_ca_system_score_gemma":0.1336661,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937617,"about_ca_topic_score_gemma":0.99247,"domain_scores_codex":[0.9956498,0.0002383324,0.0004681557,0.0004984856,0.002171049,0.0009741955],"domain_scores_gemma":[0.9634271,0.001129955,0.001243771,0.0008524943,0.0317498,0.001596844],"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":[0.00002132216,0.000008130042,0.001411377,0.000253729,0.00002056711,0.000006299719,0.00002202957,0.0001008846,0.000007626798,0.0003258535,0.9964497,0.001372481],"study_design_scores_gemma":[0.000160623,0.00001809152,0.04279709,0.0010146,0.00009201076,0.00003069915,0.0007605921,0.0005986409,0.0002108887,0.0005951842,0.9536225,0.00009899539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006861755,0.00004693465,0.00001812908,0.0001132874,0.00002692016,0.00001319303,0.9990702,0.000040039,0.0006026928],"genre_scores_gemma":[0.0007656877,0.0002389926,0.0002386646,0.000117359,0.00001812584,0.00009489674,0.9954887,0.00005498769,0.002982536],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05856698,"threshold_uncertainty_score":0.3441291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183756655441356,"score_gpt":0.2351292087582878,"score_spread":0.2132916422038743,"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."}}