{"id":"W6976158230","doi":"10.6068/dp14ba825150d67","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, Mining (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":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue; Economic statistics; Government (linguistics); Government revenue; Official statistics; Census; State (computer science); Fiscal year; Summary 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":[0.002034323,0.002287873,0.002470919,0.009236795,0.003336106,0.004697958,0.004649047,0.001319154,0.08929467],"category_scores_gemma":[0.01644425,0.001730383,0.001738386,0.04286723,0.0005684053,0.002544207,0.002024265,0.002818643,0.05744312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0595478,"about_ca_system_score_gemma":0.1409485,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.995152,"about_ca_topic_score_gemma":0.9934157,"domain_scores_codex":[0.9955428,0.0002373406,0.0004344783,0.0005118267,0.002255501,0.001018009],"domain_scores_gemma":[0.9661986,0.0009570908,0.001015434,0.0008770254,0.0294943,0.001457541],"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":[0.00002245748,0.000005978502,0.0009020165,0.0001989821,0.00001546011,0.000006527172,0.0000177632,0.00009456306,0.00000830001,0.0004098464,0.9967527,0.001565431],"study_design_scores_gemma":[0.0001085804,0.00001048449,0.0244646,0.0006312333,0.00004924362,0.00002249327,0.0003693811,0.0003826711,0.000166478,0.0005141127,0.9732097,0.00007105738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005751155,0.00004983964,0.00002162862,0.0001272333,0.00002747657,0.00001444341,0.9984298,0.00005266786,0.001219248],"genre_scores_gemma":[0.001024764,0.0003338224,0.0003914392,0.0001506574,0.00001940514,0.0001119656,0.9918556,0.0001110944,0.006001135],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08929467,"threshold_uncertainty_score":0.4320515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01948006788072072,"score_gpt":0.259473124045326,"score_spread":0.2399930561646053,"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."}}