{"id":"W6920352967","doi":"10.6068/dp14ba8d3f46d72","title":"Trend 1961 - 2013. Statistics Canada. CANSIM: Construction - Nonresidential Engineering Construction | Country: Canada | Table: Flows and stocks of fixed non-residential capital, by sector of North American Industry Classification System (NAICS) and asset | Variable: End-year gross stock, Intellectual property products (x 1,000,000), Utilities, Current prices | Units: $CAD, 1961-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-036.","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; Census; Stock (firearms); Official statistics; Descriptive statistics; Publication; Index (typography); Summary statistics; 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":[0.001371037,0.002273699,0.002236599,0.008290747,0.002797976,0.004304674,0.004560212,0.001360633,0.07763944],"category_scores_gemma":[0.01453775,0.001501604,0.001773722,0.03773905,0.0005890763,0.002316456,0.002054779,0.002774762,0.04907073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04175614,"about_ca_system_score_gemma":0.1060029,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930225,"about_ca_topic_score_gemma":0.9916523,"domain_scores_codex":[0.9968869,0.0001429506,0.0003077156,0.0004389019,0.001482001,0.0007415054],"domain_scores_gemma":[0.9715376,0.0009399708,0.0009399147,0.0007862447,0.02460154,0.001194723],"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":[0.00001990111,0.000006566182,0.001229681,0.0002115493,0.00001872166,0.000007000232,0.00001849407,0.0001335856,0.000009882177,0.0003886252,0.9964612,0.001494795],"study_design_scores_gemma":[0.0001307925,0.00001041369,0.0248309,0.0007367365,0.00005774902,0.00002484287,0.0003907125,0.0004914723,0.0001996826,0.0006392224,0.9724163,0.00007106645],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005720142,0.00004134801,0.00001964796,0.00008054509,0.00001828581,0.000008975883,0.9989979,0.00004824327,0.0007279312],"genre_scores_gemma":[0.0007599618,0.0002169469,0.0002464775,0.00009694189,0.00001369963,0.00006926737,0.9952686,0.00007064715,0.003257429],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07763944,"threshold_uncertainty_score":0.3029634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02339859374340507,"score_gpt":0.2305725840330201,"score_spread":0.207173990289615,"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."}}