{"id":"W6901621939","doi":"10.6068/dp14ba82b37d754","title":"Trend 1988 - 2008. Statistics Canada. CANSIM: Government - Revenue and Expenditures | Country: Canada | Table: Local general government revenue and expenditures, current and capital accounts, year ending December 31 | Variable: Current and capital accounts, Health, expenditures | Units: $CAD x 1,000, 1988-2008. 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; Capital expenditure; Government revenue; Government (linguistics); Official statistics; Descriptive statistics; Census; Capital (architecture)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002094074,0.002304786,0.002523084,0.009643826,0.003269191,0.004576298,0.004659321,0.00132606,0.09126616],"category_scores_gemma":[0.0162114,0.001753987,0.001725175,0.04157546,0.0005741239,0.002546226,0.002053066,0.003027839,0.05720383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06138007,"about_ca_system_score_gemma":0.149413,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950684,"about_ca_topic_score_gemma":0.993389,"domain_scores_codex":[0.9955848,0.0002467018,0.0004008584,0.0005083724,0.002200851,0.001058294],"domain_scores_gemma":[0.9662563,0.0009437247,0.001059727,0.0008825008,0.02935381,0.001503922],"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":[0.00001987104,0.000005589603,0.0008752888,0.0001911024,0.00001507366,0.000006106267,0.00001914971,0.00008927208,0.000007280649,0.000420522,0.996707,0.001643824],"study_design_scores_gemma":[0.00009669638,0.000009876339,0.02114942,0.0006457079,0.00004924236,0.00002223809,0.0003758505,0.0003627352,0.0001432167,0.0005160128,0.9765622,0.00006675891],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006881594,0.0000630991,0.0000251244,0.0001531988,0.00002954365,0.00001567901,0.9982344,0.00006515568,0.001344936],"genre_scores_gemma":[0.001168957,0.0003879016,0.0004571372,0.0001771473,0.00002194798,0.0001385006,0.9902498,0.0001322487,0.00726631],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9087338,"threshold_uncertainty_score":0.4453457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199623569371959,"score_gpt":0.2606465125094065,"score_spread":0.2406841555722106,"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."}}