{"id":"W6901693732","doi":"10.6068/dp14ba8ddcd4b7","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 Internet who do not use electronic commerce, Motor vehicle and parts dealers, Security concerns | 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; Information and Communications Technology; Census; Information technology; Summary 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":[0.002080351,0.002467857,0.00269899,0.0100572,0.003535396,0.004671681,0.005311712,0.001521495,0.06758253],"category_scores_gemma":[0.01924202,0.001679965,0.002193699,0.04588624,0.0006242403,0.002549879,0.002460114,0.003258675,0.04255409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04995057,"about_ca_system_score_gemma":0.141661,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.99392,"about_ca_topic_score_gemma":0.9924258,"domain_scores_codex":[0.9954567,0.0002530614,0.0005082643,0.0005158087,0.002247655,0.001018556],"domain_scores_gemma":[0.9578503,0.001318887,0.001330812,0.0009313661,0.03680937,0.0017592],"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":[0.00002244288,0.000008293949,0.001405146,0.0002640807,0.00002054131,0.000006338249,0.00002277857,0.00009885091,0.000007362257,0.0003129353,0.9963896,0.001441635],"study_design_scores_gemma":[0.0001784682,0.00001910115,0.04503759,0.001117147,0.00009796156,0.00003174156,0.0007889669,0.0006005339,0.0002172866,0.0006336541,0.9511755,0.0001020487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006688003,0.00004394201,0.00001893666,0.0001130499,0.00002739019,0.00001526148,0.9990209,0.00004171653,0.0006518426],"genre_scores_gemma":[0.0007803463,0.0002404716,0.0002526382,0.0001290491,0.00001890363,0.0001135132,0.9950048,0.0000621564,0.003398177],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06758253,"threshold_uncertainty_score":0.3624185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205053702673357,"score_gpt":0.2414628813010823,"score_spread":0.2209575110337466,"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."}}