{"id":"W6958000911","doi":"10.6068/dp16bd756e65818","title":"TREND: International Monetary Fund. Government Finance Statistics: Revenue: Revenue (Percent of GDP) | Country: Canada | Sector: General government | Accounting Code: G111, 1990 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 056-019-010","year":2019,"lang":"en","type":"other","venue":"Data Planet","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government revenue; Public finance; Revenue; Government (linguistics); National accounts; Market liquidity; Public sector; Economic 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":[0.001626016,0.001708533,0.001768954,0.007640973,0.000989044,0.003776057,0.003366742,0.001450521,0.1333497],"category_scores_gemma":[0.01358444,0.0009410465,0.001080209,0.01867732,0.0004605161,0.003676716,0.001867682,0.0037301,0.1783571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003811538,"about_ca_system_score_gemma":0.00874961,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1364268,"about_ca_topic_score_gemma":0.1250478,"domain_scores_codex":[0.9980027,0.0001978096,0.0003381171,0.0004900556,0.0006778092,0.000293496],"domain_scores_gemma":[0.990432,0.000952722,0.001336337,0.001084548,0.005584951,0.0006094812],"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":[0.00001368716,0.00000545218,0.0003411422,0.0001978121,0.000009581156,0.00000405448,0.000006657668,0.00004580518,0.000008410617,0.0002563169,0.9978351,0.001276034],"study_design_scores_gemma":[0.00009528857,0.000005919765,0.004487194,0.0003189289,0.00001970015,0.00001521417,0.00006752011,0.00011519,0.00006958939,0.000800657,0.9939819,0.00002286923],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002237975,0.00002779438,0.00002378987,0.00006341548,0.00002145718,0.000007418769,0.9992597,0.00006270942,0.000511397],"genre_scores_gemma":[0.000241609,0.00009274622,0.0001914751,0.00005251078,0.00001751143,0.0001056424,0.9981974,0.00009138961,0.001009714],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8635732,"threshold_uncertainty_score":0.4460992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05827954563469567,"score_gpt":0.2789961861666957,"score_spread":0.220716640532,"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."}}