{"id":"W6901737788","doi":"10.6068/dp14ba8ede9fb14","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/non-users of Internet who do not use electronic commerce, Textile mills, Prefer to maintain current business model | 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; Descriptive statistics; Information technology; Information and Communications Technology; 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":[],"consensus_categories":[],"category_scores_codex":[0.001926683,0.002371487,0.002589799,0.009660603,0.003326548,0.004562992,0.005006335,0.001463233,0.06296406],"category_scores_gemma":[0.01707704,0.001545511,0.002131364,0.04346985,0.0006114612,0.002459184,0.002362229,0.003090994,0.04165223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04639223,"about_ca_system_score_gemma":0.1275066,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935678,"about_ca_topic_score_gemma":0.9923865,"domain_scores_codex":[0.9958872,0.0002258411,0.0004381074,0.0004819107,0.002025088,0.0009418852],"domain_scores_gemma":[0.9629326,0.001126563,0.001242024,0.0008561832,0.03218218,0.001660414],"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":[0.00002346632,0.000008942176,0.001601104,0.0002504569,0.00002047911,0.000006499698,0.0000236042,0.00009992109,0.000008169523,0.0003200324,0.9961389,0.001498473],"study_design_scores_gemma":[0.0001668725,0.00001910942,0.04661445,0.0009775163,0.00008822315,0.00003113923,0.000805901,0.0006160199,0.000220596,0.0005753873,0.9497841,0.000100743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007631553,0.00004353436,0.00001872716,0.0001075371,0.0000263706,0.00001411271,0.999018,0.00004263703,0.0006528943],"genre_scores_gemma":[0.0008060155,0.0002199576,0.000240964,0.0001174716,0.00001776911,0.00009817531,0.9950971,0.00005670962,0.003345898],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06296406,"threshold_uncertainty_score":0.3366008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159626904997274,"score_gpt":0.2453685944448805,"score_spread":0.2237723253949078,"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."}}