{"id":"W3162343456","doi":"","title":"Cutting Through the Blue Ribbon: A Balanced Look at Alberta's Finances","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Government (linguistics); Per capita; Debt; Economics; Fiscal sustainability; Business; Finance; Population","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001838577,0.0004684495,0.0003010293,0.002820844,0.01068073,0.01230724,0.001600801,0.003226237,0.01030407],"category_scores_gemma":[0.002980554,0.0004846652,0.0003043076,0.003729532,0.002503712,0.002185029,0.001681166,0.004431573,0.0009156914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08688655,"about_ca_system_score_gemma":0.1576629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9708074,"about_ca_topic_score_gemma":0.9904293,"domain_scores_codex":[0.9971141,0.000153579,0.0000353231,0.00008923464,0.001504061,0.0011038],"domain_scores_gemma":[0.9968232,0.0003110431,0.00009253185,0.00005833779,0.001345182,0.001369725],"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":[0.00004192357,0.00004635881,0.008182515,0.0001546678,0.00001697647,0.0008377865,0.002408026,0.0007754961,0.0005069365,0.06685691,0.8659745,0.05419795],"study_design_scores_gemma":[0.00001166645,0.00001845384,0.02408832,0.0004057302,0.00001211423,0.0001228143,0.00685691,0.0003375109,0.0001843154,0.00651248,0.9613861,0.00006354888],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04413138,0.03314032,0.0006965421,0.5864385,0.005425322,0.00009456665,0.002162883,0.0002489199,0.3276615],"genre_scores_gemma":[0.3773466,0.04226256,0.003661515,0.1097173,0.001763237,0.00006990752,0.00289957,0.0002905692,0.4619888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08688655,"threshold_uncertainty_score":0.630409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009524895678976799,"score_gpt":0.2735864397659747,"score_spread":0.2640615440869979,"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."}}