{"id":"W6958109947","doi":"10.6068/dp14ba83b32d484","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: Internal revenue or expenditures (x 1,000,000), Revenue | 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; Government (linguistics); Economic statistics; Government revenue; Census; Official statistics; Publication; 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":[],"consensus_categories":[],"category_scores_codex":[0.002169335,0.002502783,0.002505325,0.009566255,0.003227717,0.005075621,0.00488739,0.001388962,0.08288009],"category_scores_gemma":[0.01818185,0.00173535,0.00191617,0.04317826,0.000634516,0.002514282,0.002165762,0.003068261,0.0564639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05695829,"about_ca_system_score_gemma":0.1373741,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994082,"about_ca_topic_score_gemma":0.9929631,"domain_scores_codex":[0.9953301,0.0002656751,0.0004707903,0.0005660146,0.002325626,0.001041827],"domain_scores_gemma":[0.9634875,0.001199252,0.001183934,0.001130432,0.03146604,0.001532874],"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":[0.00002002944,0.000005421357,0.0007498295,0.0001931966,0.00001714901,0.000005424393,0.00001529907,0.00008919399,0.000007911754,0.0003843623,0.9971438,0.001368373],"study_design_scores_gemma":[0.0001114708,0.000008696116,0.01816441,0.0006425923,0.00005217068,0.00001908358,0.0002992146,0.0003667074,0.0001722568,0.0005567476,0.9795364,0.00007030315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004324111,0.00004308757,0.00001941024,0.0001106393,0.00002289044,0.00001175616,0.9987807,0.00005241169,0.0009158505],"genre_scores_gemma":[0.0006784031,0.0002391657,0.0003281709,0.0001196332,0.00001535029,0.00009136026,0.9946408,0.0000939097,0.003793051],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08288009,"threshold_uncertainty_score":0.4132633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087756192523239,"score_gpt":0.2541663083673293,"score_spread":0.2332887464420969,"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."}}