{"id":"W6957889229","doi":"10.6068/dp14ba8ede3a512","title":"Trend 2006 - 2007. Statistics Canada. CANSIM: Information and Communications Technology - Business and Government Internet Use | Country: Canada | Table: Survey of electronic commerce and technology, characteristics of Web sites, by North American Industry Classification System (NAICS) | Variable: Enterprises offering application for collecting customer information online, Travel arrangement and reservation services | Units: %, 2006-2007. 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":"Official statistics; Economic statistics; The Internet; Business statistics; Government (linguistics); Census; Information technology; Reservation; Information and Communications 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":[0.002154559,0.002483194,0.002930737,0.01004221,0.003629485,0.004894309,0.005446053,0.001453463,0.06588452],"category_scores_gemma":[0.01849245,0.001704721,0.002133748,0.04840142,0.0006776691,0.002524352,0.002543705,0.00326317,0.04428227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05199828,"about_ca_system_score_gemma":0.1487658,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937224,"about_ca_topic_score_gemma":0.99218,"domain_scores_codex":[0.9950135,0.0002865858,0.0005115949,0.0005561538,0.002533806,0.001098227],"domain_scores_gemma":[0.9542428,0.001422485,0.001327475,0.001051922,0.04004141,0.001913911],"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":[0.00002207357,0.000008231507,0.001438417,0.0002521916,0.00002173227,0.00000653919,0.00002390857,0.0001007834,0.000008346778,0.0003171091,0.9964065,0.001394171],"study_design_scores_gemma":[0.0001593798,0.00001793007,0.04060772,0.001051264,0.00008888679,0.00002915452,0.0008310757,0.0005545622,0.0002170835,0.0006138083,0.9557295,0.00009977575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006521086,0.00003988443,0.00002066132,0.000112644,0.00002686808,0.00001595207,0.9990275,0.00004150609,0.0006498826],"genre_scores_gemma":[0.0007983352,0.0002344633,0.0003060152,0.0001363937,0.00001898812,0.0001260994,0.9949823,0.0000694579,0.003327974],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06588452,"threshold_uncertainty_score":0.3772756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02589345483412425,"score_gpt":0.2466529181721355,"score_spread":0.2207594633380112,"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."}}