{"id":"W6976465400","doi":"10.6068/dp14ba8c339cb99","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: Non-users of Internet, Furniture and home furnishings stores, Lack of skilled employees | 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; Official statistics; Government (linguistics); The Internet; Economic statistics; Census; Information and Communications Technology; Information technology; Telephone number","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":[0.002030796,0.00251475,0.002779829,0.009918721,0.003503966,0.00463344,0.005420549,0.001530062,0.06611054],"category_scores_gemma":[0.01873887,0.001657348,0.002223517,0.04627667,0.0006405758,0.002510565,0.002448895,0.003229126,0.04266251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04820426,"about_ca_system_score_gemma":0.1372422,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939552,"about_ca_topic_score_gemma":0.9924746,"domain_scores_codex":[0.995669,0.00024876,0.0004927816,0.0005179705,0.002097751,0.0009736932],"domain_scores_gemma":[0.9609562,0.001247756,0.00123958,0.0009065206,0.03399774,0.001652248],"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":[0.00002239486,0.000008086744,0.001346482,0.0002780776,0.00002106115,0.000006274151,0.00002275653,0.00009910804,0.000007729447,0.0003032723,0.9964628,0.001422005],"study_design_scores_gemma":[0.0001843746,0.00001816914,0.04179135,0.00113967,0.00009701191,0.00003186905,0.000760799,0.0005834397,0.0002048828,0.0006409149,0.9544461,0.0001013597],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006066146,0.00004487872,0.00001775657,0.0001036733,0.00002552284,0.00001397707,0.9991167,0.00003993751,0.0005768879],"genre_scores_gemma":[0.0007331404,0.0002384101,0.0002464362,0.0001225576,0.00001791903,0.0001047835,0.9954787,0.00005930695,0.002998736],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06611054,"threshold_uncertainty_score":0.349748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739976798681845,"score_gpt":0.2406296599009193,"score_spread":0.2232298919141008,"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."}}