{"id":"W6920220396","doi":"10.6068/dp14ba8cc7ee378","title":"Trend 2000 - 2007. Statistics Canada. CANSIM: Information and Communications Technology - Business and Government Internet Use | Country: Canada | Table: Survey of electronic commerce and technology, use of information and communication technologies, by North American Industry Classification System (NAICS) | Variable: Enterprises that are presently using it, Waste management and remediation services, Extranet | Units: %, 2000-2007. 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":"Economic statistics; Business statistics; Official statistics; The Internet; Government (linguistics); 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":[0.001953385,0.002470604,0.002697815,0.009972848,0.003329057,0.004846966,0.005217546,0.001575871,0.06430586],"category_scores_gemma":[0.01762884,0.001677348,0.002256389,0.04642826,0.0006566816,0.002534093,0.002498793,0.003307518,0.04290128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04864945,"about_ca_system_score_gemma":0.1340679,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930021,"about_ca_topic_score_gemma":0.9913422,"domain_scores_codex":[0.9954208,0.0002413467,0.0005101797,0.0005071624,0.002290238,0.001030418],"domain_scores_gemma":[0.9611456,0.001250739,0.001227313,0.0008910493,0.03400021,0.001485062],"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":[0.00002162821,0.000008159867,0.001291566,0.0002710072,0.00002209363,0.000006389276,0.00002080629,0.0001009449,0.000008078641,0.0003073427,0.9965891,0.001352877],"study_design_scores_gemma":[0.0001649821,0.00001583053,0.03694881,0.001030368,0.00009190646,0.00002712468,0.0007354225,0.0005391694,0.0002220958,0.0005948653,0.9595355,0.0000940073],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006083444,0.000041693,0.0000168418,0.0001043789,0.00002460969,0.00001354667,0.9990793,0.00003877615,0.0006199985],"genre_scores_gemma":[0.0006889085,0.0002179455,0.0002310651,0.0001118195,0.00001657639,0.0001078564,0.9954786,0.0000586008,0.003088662],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06430586,"threshold_uncertainty_score":0.3529781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03179823956501483,"score_gpt":0.2391759471068597,"score_spread":0.2073777075418449,"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."}}