{"id":"W6976896936","doi":"10.6068/dp14ba8f8ee3c23","title":"Trend 1995 - 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: Geometric (infinite) end-year net stock, Commercial buildings (x 1,000,000), Management of companies and enterprises, Current prices | Units: $CAD, 1995-2013. 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); Descriptive statistics; Official statistics; Summary statistics; Index (typography); Publication; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006192301,0.0009375139,0.001771812,0.0006544255,0.0001518968,0.0002369828,0.0011015,0.0004144179,0.0002519936],"category_scores_gemma":[0.00006632614,0.001031204,7.060937e-7,0.001105333,0.0008814501,0.0004578335,0.0008202285,0.001029373,0.000001826893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004075895,"about_ca_system_score_gemma":0.002462436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9989969,"about_ca_topic_score_gemma":0.991042,"domain_scores_codex":[0.9942118,0.0003836669,0.001778741,0.001255165,0.001669525,0.0007011237],"domain_scores_gemma":[0.994003,0.0006116852,0.003082902,0.001516121,0.0002363552,0.0005499354],"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":[0.0002990439,0.00009147907,0.002253765,0.004358145,0.0009961211,0.00003425319,0.000008157192,0.0001582376,0.00003027166,0.000137987,0.9910465,0.0005860083],"study_design_scores_gemma":[0.001679552,0.0001744922,0.00130481,0.0002865507,0.001557461,0.0001675516,0.0009672813,0.008647913,4.527447e-7,4.059141e-8,0.9843616,0.0008522333],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001479387,0.001886859,0.0001459587,7.173502e-7,0.0009645852,0.001285007,0.9941021,0.00006972141,0.00006569648],"genre_scores_gemma":[0.007818528,0.001694722,0.001198588,0.000005028865,0.0001612311,0.00003080079,0.9888377,0.0002318103,0.00002157811],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.008489676,"threshold_uncertainty_score":0.9992138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749186971436007,"score_gpt":0.2337047282306429,"score_spread":0.2162128585162829,"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."}}