{"id":"W6901773076","doi":"10.6068/dp14baa3ac33846","title":"Trend 1961 - 2012. Statistics Canada. CANSIM: Economic Accounts - Income and Expenditure Accounts | Country: Canada | Province: British Columbia | Table: Maintenance and repair expenditures in housing | Variable: Landlord and tenant occupied expenditures (x 1,000,000) | Units: $CAD, 1961-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-062.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Landlord; Census; National accounts; Official statistics; Wages and salaries; Summary statistics; Payroll; Personal income; National Income and Product 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":[0.001846927,0.002265081,0.002365118,0.008403579,0.002935884,0.004617082,0.004329154,0.00136011,0.113207],"category_scores_gemma":[0.01674016,0.001594193,0.001732737,0.04186383,0.0005661198,0.002691852,0.002036765,0.002823775,0.0763985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0432777,"about_ca_system_score_gemma":0.1017528,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9917768,"about_ca_topic_score_gemma":0.9902714,"domain_scores_codex":[0.9964367,0.0002128585,0.0003604821,0.0004968527,0.001688115,0.000804957],"domain_scores_gemma":[0.9698644,0.0009991868,0.0009005953,0.0009659029,0.02598983,0.001280202],"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":[0.00001531022,0.000004231798,0.0006462718,0.0001627684,0.00001248757,0.000005491846,0.00001567129,0.00007803786,0.000007805471,0.0003254091,0.9973385,0.001388062],"study_design_scores_gemma":[0.00008392514,0.000007085288,0.01556762,0.0005576368,0.0000382276,0.00001861669,0.0002988372,0.0003157184,0.0001317505,0.0005029017,0.9824167,0.00006088281],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004273476,0.00003924425,0.0000223007,0.00009231871,0.00002317107,0.00001107118,0.9987005,0.00005585716,0.001012869],"genre_scores_gemma":[0.0006581579,0.0002363236,0.0003379314,0.0001147827,0.00001636661,0.0001006059,0.9930434,0.0001164189,0.005376047],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.113207,"threshold_uncertainty_score":0.3787152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144616548207959,"score_gpt":0.2336496724793106,"score_spread":0.2191880176585147,"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."}}