{"id":"W6976807630","doi":"10.6068/dp14ba81d571e27","title":"Trend 1994 - 2005. 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: Other adjustments (x 1,000,000), Expenditures | Units: $CAD, 1994-2005. 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 (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":[0.0022615,0.00251002,0.002523815,0.009594583,0.003253985,0.005110579,0.00480714,0.001409257,0.08963022],"category_scores_gemma":[0.0185063,0.001773222,0.00188572,0.04347204,0.0006283569,0.002521326,0.002097341,0.003055451,0.05711398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05920361,"about_ca_system_score_gemma":0.1394439,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9942923,"about_ca_topic_score_gemma":0.9929558,"domain_scores_codex":[0.9952225,0.000282953,0.0004911032,0.0005806157,0.002365187,0.001057651],"domain_scores_gemma":[0.9617077,0.001268099,0.001275292,0.001141607,0.03295648,0.001650815],"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":[0.00001974174,0.000005468882,0.0007210802,0.0001854495,0.00001693471,0.000005060581,0.00001514827,0.00009121681,0.000007072934,0.0003710049,0.9972188,0.001342997],"study_design_scores_gemma":[0.000115059,0.000009331075,0.01793444,0.0006174968,0.00005142556,0.00001887525,0.0003027641,0.0003775242,0.0001675165,0.0005596589,0.9797745,0.00007129397],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004555464,0.00004198559,0.00001968476,0.0001150505,0.00002347623,0.00001206526,0.998744,0.0000563769,0.0009418279],"genre_scores_gemma":[0.0007248995,0.0002328215,0.0003323455,0.0001274682,0.00001571185,0.00009231478,0.9943369,0.000101917,0.004035651],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08963022,"threshold_uncertainty_score":0.4295543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02011312334303672,"score_gpt":0.2499770338856845,"score_spread":0.2298639105426478,"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."}}