{"id":"W6976711168","doi":"10.6068/dp14ba7ee4d6480","title":"Trend 1988 - 2005. Statistics Canada. CANSIM: Government - Revenue and Expenditures | Country: Canada | Table: Local general government revenue and expenditures, year ending December 31 | Variable: Total revenue | Units: $CAD x 1,000, 1988-2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-107.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue; Government revenue; Economic statistics; Government (linguistics); Census; Official statistics; Total revenue; State (computer science); 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002104446,0.002338172,0.00243572,0.01017946,0.003306003,0.004776557,0.004631967,0.001302822,0.09671474],"category_scores_gemma":[0.01564408,0.001799452,0.001705276,0.04538767,0.0005659951,0.0025234,0.001947326,0.002848448,0.05709583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06830859,"about_ca_system_score_gemma":0.1591249,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9953781,"about_ca_topic_score_gemma":0.9934942,"domain_scores_codex":[0.9953647,0.0002490784,0.0004388348,0.0004991475,0.00235857,0.001089718],"domain_scores_gemma":[0.9653045,0.0009499865,0.001091242,0.0008653976,0.03027037,0.001518517],"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":[0.00002060943,0.000006022968,0.0008503643,0.000203603,0.00001570597,0.000006232711,0.00001961031,0.00009614947,0.000007770528,0.0004622529,0.9964915,0.001820214],"study_design_scores_gemma":[0.00009298063,0.00001085412,0.02308271,0.0006345867,0.00004989441,0.00002286686,0.0003992213,0.0003527658,0.0001525043,0.0005065809,0.9746295,0.00006560497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007256489,0.00006773216,0.00002562813,0.0001511217,0.00003212998,0.00001838451,0.9979531,0.00006952087,0.001609825],"genre_scores_gemma":[0.001280881,0.0004179047,0.0004539658,0.000181127,0.00002270286,0.0001421483,0.9889892,0.0001409215,0.008371126],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9032853,"threshold_uncertainty_score":0.4956158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622878327318632,"score_gpt":0.2367737163652205,"score_spread":0.2205449330920342,"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."}}