{"id":"W6958014499","doi":"10.6068/dp14ba887e81687","title":"Trend 1961 - 2003. Statistics Canada. CANSIM: Construction - Nonresidential Building Construction | Country: Canada | Table: Flows and stocks of fixed non-residential capital, by sector of North American Industry Classification System (NAICS) and asset | Variable: Investment, Transportation engineering (x 1,000,000), Utilities, | Units: $CAD Chained (2007) $CAD, 1961-2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-035.","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; Official statistics; Stock (firearms); Summary statistics; Index (typography); Publication; Descriptive statistics; Capital (architecture)","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":[0.001301195,0.002319128,0.002260752,0.008122162,0.00275604,0.004058404,0.004552231,0.001334766,0.07073902],"category_scores_gemma":[0.01273911,0.001520071,0.001758463,0.0375626,0.0005938117,0.002135776,0.001896529,0.002688868,0.04438324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0443171,"about_ca_system_score_gemma":0.1023693,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939738,"about_ca_topic_score_gemma":0.9924178,"domain_scores_codex":[0.9969857,0.0001421737,0.0002828735,0.0004310803,0.001417403,0.0007407438],"domain_scores_gemma":[0.9747448,0.0008425038,0.0009429118,0.0007018897,0.02162884,0.001139032],"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":[0.00002443895,0.000007817844,0.001535857,0.0002198304,0.00002086566,0.000007966861,0.00002085066,0.000160751,0.000009756977,0.0003980975,0.9960397,0.001553867],"study_design_scores_gemma":[0.0001483032,0.000012647,0.03214166,0.0006917447,0.00006083235,0.00002790364,0.0004592439,0.0006063204,0.0002222239,0.0005877684,0.9649668,0.00007459323],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006933836,0.00004019263,0.00001762217,0.00007564337,0.00001628456,0.000008369161,0.9990537,0.0000454231,0.0006735403],"genre_scores_gemma":[0.0008976243,0.0002037971,0.0002279367,0.00008252329,0.00001243757,0.00006424291,0.9951676,0.00006191865,0.003282005],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07073902,"threshold_uncertainty_score":0.3215445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567097124230751,"score_gpt":0.223651182385438,"score_spread":0.2079802111431305,"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."}}