{"id":"W6939194379","doi":"10.6068/dp14ba8f5b0d075","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, Gasoline stations, Cost of development and maintenance is too high | 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); Economic statistics; Official statistics; Census; Telephone number; Information and Communications Technology; Information technology","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":[0.001891881,0.002426884,0.002744343,0.01037255,0.003386621,0.004486108,0.005181408,0.001524517,0.06210651],"category_scores_gemma":[0.01816934,0.001640628,0.002187629,0.0477943,0.0006409877,0.002488141,0.002394045,0.003172797,0.03703111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05109834,"about_ca_system_score_gemma":0.1398406,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9943233,"about_ca_topic_score_gemma":0.9929391,"domain_scores_codex":[0.9955286,0.0002386842,0.0005063476,0.0005034305,0.002200874,0.001022067],"domain_scores_gemma":[0.960041,0.001247126,0.001379524,0.0008790397,0.03467965,0.001773698],"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":[0.00002460695,0.000009052942,0.001564765,0.0002979925,0.00002336336,0.000006855981,0.00002340664,0.0001144318,0.000008070858,0.0003681204,0.9961046,0.001454712],"study_design_scores_gemma":[0.0001871334,0.00002088336,0.04891632,0.00108047,0.0001054045,0.00003382754,0.0008065897,0.0006476221,0.0002327095,0.000628259,0.9472345,0.000106218],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007273613,0.0000470611,0.00001802505,0.0001092118,0.00002582848,0.00001422342,0.9990362,0.00003905738,0.0006376646],"genre_scores_gemma":[0.0009280707,0.0002560642,0.0002486271,0.0001251891,0.00001803637,0.000102645,0.9949396,0.00005632306,0.003325442],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06210651,"threshold_uncertainty_score":0.3707462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740284460297635,"score_gpt":0.2344497669103054,"score_spread":0.2170469223073291,"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."}}