{"id":"W6976509431","doi":"10.6068/dp14ba81d0ae297","title":"Trend 1980 - 2005. Statistics Canada. CANSIM: Government - Government Business Enterprises | Country: Canada | Table: Assets, liabilities and net worth of provincial and territorial government business enterprises, by industry, as at the end of the fiscal year closest to December 31 | Variable: Total liabilities and net worth, Trade | Units: $CAD x 1,000, 1980-2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-105.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Economic statistics; Census; Official statistics; Goods and services; Business statistics; State (computer science); Politics; National accounts","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":[0.001562924,0.002293049,0.002446637,0.009229589,0.003021321,0.004812353,0.004828961,0.001380611,0.06653877],"category_scores_gemma":[0.01433019,0.001640638,0.001701745,0.04205396,0.0006242463,0.002495392,0.002003488,0.002982167,0.04577522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05432669,"about_ca_system_score_gemma":0.1284646,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994848,"about_ca_topic_score_gemma":0.993636,"domain_scores_codex":[0.9963232,0.0001765633,0.0003676083,0.000474152,0.001773696,0.000884805],"domain_scores_gemma":[0.9701283,0.0009220159,0.001087522,0.0007961352,0.02574386,0.001322072],"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":[0.00001991688,0.000006900797,0.001183731,0.0002025606,0.00001815332,0.000007520765,0.00002091675,0.0001153289,0.000008800022,0.0004225576,0.9965625,0.001431028],"study_design_scores_gemma":[0.0001152444,0.00001076165,0.02601248,0.0006919176,0.00005499579,0.00002589363,0.0004561261,0.0004679059,0.0001912408,0.000597517,0.9713026,0.00007335342],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006789755,0.00005002204,0.00001766874,0.00009801792,0.00001977079,0.00001030852,0.9988666,0.00004615565,0.0008236833],"genre_scores_gemma":[0.0009037934,0.000258376,0.0002520961,0.0001067288,0.00001484269,0.00007736282,0.9944959,0.00007014637,0.003820778],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06653877,"threshold_uncertainty_score":0.3941695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009799719815439064,"score_gpt":0.2137641351945271,"score_spread":0.203964415379088,"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."}}