{"id":"W6938811341","doi":"10.6068/dp14ba857a69a85","title":"Trend 1989 - 2009. Statistics Canada. CANSIM: Government - Revenue and Expenditures | Country: Canada | Table: Federal, provincial and territorial general government revenue and expenditures, for fiscal year ending March 31 | Variable: Federal general government, Oil and gas royalties (x 1,000,000) | Units: $CAD, 1989-2009. 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); Official statistics; Government revenue; Census; State (computer science); Summary statistics; Fiscal year","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":[0.001785244,0.002255294,0.002391009,0.008248256,0.002923739,0.004359856,0.004498965,0.001294423,0.07869188],"category_scores_gemma":[0.01478547,0.001580552,0.001641197,0.03955446,0.0005566772,0.002293888,0.001861314,0.002665844,0.05280681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05215137,"about_ca_system_score_gemma":0.1152229,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9942011,"about_ca_topic_score_gemma":0.9926848,"domain_scores_codex":[0.996309,0.0001980346,0.0003559083,0.0004783073,0.001777811,0.0008808437],"domain_scores_gemma":[0.9720243,0.0008684485,0.0009733437,0.0008227961,0.02405362,0.001257446],"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":[0.00002234339,0.000005878198,0.0009741381,0.0001812009,0.00001625143,0.000006324762,0.0000162591,0.0001021313,0.000008144082,0.0003750403,0.9969213,0.001371072],"study_design_scores_gemma":[0.0001217379,0.00001020358,0.0243577,0.0005994449,0.00004876463,0.00002232173,0.0003382771,0.0004176945,0.0001807942,0.0005100569,0.9733253,0.00006765354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005188455,0.00003909395,0.00001696463,0.00009748898,0.00001961273,0.000009941589,0.9988753,0.00004334972,0.0008463848],"genre_scores_gemma":[0.0008927395,0.0002467928,0.000303872,0.0001139961,0.00001512355,0.0000837606,0.9940352,0.00008424303,0.004224308],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07869188,"threshold_uncertainty_score":0.3783864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01699926070756336,"score_gpt":0.2396430464341674,"score_spread":0.222643785726604,"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."}}