{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001059329,0.0007472145,0.001027555,0.00003669697,0.0009805731,0.001107511,0.001301328,0.0003940472,0.0005268092],"category_scores_gemma":[0.000210989,0.0007432319,8.673757e-7,0.00009474769,0.0003203436,0.0004121977,0.001107566,0.0006153586,0.000001092897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000807122,"about_ca_system_score_gemma":0.003889922,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9984168,"about_ca_topic_score_gemma":0.998993,"domain_scores_codex":[0.9932293,0.0005504819,0.0008288942,0.001466113,0.002796765,0.001128466],"domain_scores_gemma":[0.9967002,0.0006370686,0.000770582,0.001101962,0.000018736,0.0007714126],"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":[0.0001773874,0.00005095757,0.0001856428,0.0002578987,0.0002864701,0.0002414533,0.00002564937,0.000005212492,0.00001570877,0.0009297436,0.9971638,0.0006600144],"study_design_scores_gemma":[0.001149626,0.0001059202,0.00002060112,0.00007492099,0.000352092,0.00003901503,0.001081165,0.0009212266,1.232978e-7,0.000001271983,0.9953798,0.0008742266],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001276858,0.00139719,0.0000219616,0.00001689526,0.001724569,0.0008217127,0.9932529,0.00002257146,0.002729424],"genre_scores_gemma":[0.00006488062,0.0009619878,0.0008477519,0.0002356627,0.004170118,0.00005230478,0.9786823,0.0001532882,0.01483169],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01457059,"threshold_uncertainty_score":0.9999294,"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."}}