{"id":"W6939118504","doi":"10.6068/dp14ba83aedaa57","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: Deferred charges and other assets, Manufacturing | 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":[0.001562941,0.002286556,0.00245586,0.009305133,0.003017607,0.004840159,0.004892017,0.001403888,0.06661627],"category_scores_gemma":[0.01431355,0.001686875,0.001736811,0.04216184,0.0006189148,0.002472807,0.001994968,0.00301373,0.04429524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05683424,"about_ca_system_score_gemma":0.1312801,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951131,"about_ca_topic_score_gemma":0.993835,"domain_scores_codex":[0.9961534,0.0001796192,0.0003806843,0.0004792716,0.00188214,0.0009247501],"domain_scores_gemma":[0.9698502,0.0009129716,0.001121344,0.0007741723,0.02599999,0.001341311],"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":[0.00002038382,0.000007317832,0.001233198,0.0002058356,0.00001861484,0.000007795857,0.00002128142,0.0001206863,0.000008743718,0.0004445001,0.9964643,0.00144734],"study_design_scores_gemma":[0.0001173781,0.00001140602,0.02741571,0.0006985272,0.00005656897,0.00002643871,0.0004721385,0.000481922,0.000198109,0.0005968626,0.9698504,0.00007452378],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007321586,0.00005303211,0.00001828551,0.0001051872,0.0000209134,0.0000109653,0.9987773,0.00004645693,0.0008946063],"genre_scores_gemma":[0.001014131,0.0002850714,0.0002642002,0.000118668,0.0000162585,0.00008173247,0.9938058,0.00007405281,0.004340116],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06661627,"threshold_uncertainty_score":0.4123632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01229599842677447,"score_gpt":0.221796288146972,"score_spread":0.2095002897201976,"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."}}