{"id":"W6957923713","doi":"10.6068/dp14ba87236ae64","title":"Trend 1994 - 2009. Statistics Canada. CANSIM: Government - Revenue and Expenditures | Country: Canada | Table: Reconciliation of estimated federal government revenue and expenditures from budgetary documents to the Financial Management System (FMS), for fiscal year ending March 31 | Variable: Net gain or loss on exchange (x 1,000,000), Revenue minus expenditures | Units: $CAD, 1994-2009. 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; Economic statistics; Government (linguistics); Government revenue; Census; Official statistics; Descriptive statistics; Publication; State (computer science)","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":[0.001982748,0.00254114,0.002504806,0.009277799,0.003109505,0.004784,0.004820997,0.001424739,0.0702645],"category_scores_gemma":[0.01665223,0.001633909,0.00194233,0.0407629,0.0006337122,0.002370819,0.002078597,0.003022551,0.04963471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05494905,"about_ca_system_score_gemma":0.1270441,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939487,"about_ca_topic_score_gemma":0.993204,"domain_scores_codex":[0.9957599,0.0002409901,0.0004181723,0.0005530202,0.002071053,0.0009568446],"domain_scores_gemma":[0.9676039,0.001058853,0.001121616,0.001046475,0.02777193,0.001397251],"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":[0.00002125897,0.00000566944,0.0008428422,0.0001954476,0.00001940401,0.000005607008,0.00001486118,0.0001027218,0.000008820802,0.0003895989,0.997094,0.001299716],"study_design_scores_gemma":[0.0001134941,0.000008949446,0.0187818,0.0006150304,0.00005466507,0.00001992154,0.0002960601,0.0004097154,0.0001861096,0.0005340767,0.9789069,0.00007317751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004541981,0.00004422133,0.00001754309,0.0001025589,0.00002180913,0.00000969241,0.9989402,0.00004826869,0.0007703902],"genre_scores_gemma":[0.0006845896,0.0002169832,0.0002939562,0.0001071362,0.00001374402,0.00007561531,0.9954227,0.00007745293,0.003107873],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0702645,"threshold_uncertainty_score":0.3986851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365879768196939,"score_gpt":0.2538759261180177,"score_spread":0.2302171284360484,"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."}}