{"id":"W6939315269","doi":"10.6068/dp14ba8a5f56922","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, Pipeline transportation, Internet | 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":"Business statistics; Official statistics; The Internet; Economic statistics; Government (linguistics); Information and Communications Technology; Census; Information technology; Summary statistics; Descriptive 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":[0.002114833,0.002450238,0.002775184,0.009950699,0.003646864,0.004881358,0.005396235,0.001523128,0.06652594],"category_scores_gemma":[0.01820798,0.001734122,0.00219208,0.04667866,0.0006569179,0.002695818,0.002540066,0.003394121,0.04323116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05324836,"about_ca_system_score_gemma":0.1494609,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941534,"about_ca_topic_score_gemma":0.9929369,"domain_scores_codex":[0.9951793,0.0002735729,0.0005022106,0.0005224799,0.002438924,0.001083591],"domain_scores_gemma":[0.9571203,0.001266544,0.00125804,0.0009298648,0.0376607,0.00176465],"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":[0.00002233311,0.000008438597,0.001434319,0.0002646345,0.0000219275,0.000006720615,0.00002429747,0.0001002091,0.000008034714,0.0003483735,0.9963075,0.001453219],"study_design_scores_gemma":[0.0001694405,0.00001830531,0.04263949,0.001061342,0.0000946951,0.00003007253,0.0008439072,0.0005743362,0.0002145617,0.0006571488,0.9535949,0.0001018074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007141619,0.00004488276,0.00002129317,0.000121684,0.00002742315,0.00001747376,0.9989198,0.0000425117,0.0007333578],"genre_scores_gemma":[0.0008354475,0.0002490434,0.0003031425,0.0001406841,0.00001900762,0.0001348391,0.9946485,0.00007181832,0.003597423],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06652594,"threshold_uncertainty_score":0.3863457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03635227580917512,"score_gpt":0.2477810334450104,"score_spread":0.2114287576358353,"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."}}