{"id":"W6920330839","doi":"10.6068/dp14ba8e3b81d22","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 sell, Credit intermediation and related activities, Customers are not ready | 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":"Business statistics; The Internet; Official statistics; Government (linguistics); Economic statistics; Information and Communications Technology; Information technology; Descriptive statistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001985496,0.002504621,0.00267946,0.01009149,0.003510761,0.004735902,0.005238931,0.001529603,0.06456168],"category_scores_gemma":[0.0179371,0.001605741,0.002128938,0.04663168,0.0006389892,0.002520098,0.002417289,0.003199941,0.04318895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04835395,"about_ca_system_score_gemma":0.1380106,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937848,"about_ca_topic_score_gemma":0.9923009,"domain_scores_codex":[0.9955538,0.0002439203,0.0004791211,0.000509441,0.002211961,0.001001807],"domain_scores_gemma":[0.9613308,0.00121817,0.001251854,0.0008867234,0.03368571,0.001626729],"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":[0.00002126078,0.000008106228,0.001339867,0.0002517156,0.00001971134,0.000006292856,0.00002232335,0.0001026659,0.000007558712,0.0003177916,0.9965107,0.001392012],"study_design_scores_gemma":[0.000162299,0.00001705902,0.03968664,0.0009892262,0.00008609438,0.00002868528,0.0007423236,0.000570281,0.0002082532,0.0005945931,0.9568193,0.00009522521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000647596,0.00004418899,0.00001765397,0.0001079995,0.00002629953,0.00001361512,0.9990602,0.00004073246,0.0006245846],"genre_scores_gemma":[0.0007655203,0.0002320518,0.0002362433,0.0001161636,0.00001828412,0.00009867085,0.9952255,0.00005920113,0.003248279],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9354383,"threshold_uncertainty_score":0.3508341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0181204505359962,"score_gpt":0.2280641966271643,"score_spread":0.2099437460911681,"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."}}