{"id":"W6939014715","doi":"10.6068/dp14ba87cefc471","title":"Trend 1998 - 2011. Statistics Canada. CANSIM: Construction - Nonresidential Engineering Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Canals and waterways (x 1,000,000), Public administration | Units: $CAD, 1998-2011. 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; Descriptive statistics; Asset (computer security); Official statistics; Stock (firearms); Capital (architecture); Summary statistics; Index (typography)","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":[0.001486609,0.002288132,0.00230415,0.00792186,0.002854956,0.004414529,0.00457136,0.001352318,0.09084006],"category_scores_gemma":[0.01400908,0.001645627,0.00174028,0.03588369,0.0005593691,0.00240845,0.002024495,0.002757258,0.05418425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04351218,"about_ca_system_score_gemma":0.1061841,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9928597,"about_ca_topic_score_gemma":0.9917256,"domain_scores_codex":[0.9963906,0.0001779795,0.0003460913,0.0004812434,0.001788792,0.0008151764],"domain_scores_gemma":[0.9713948,0.0009131375,0.0009419727,0.0007776791,0.02469903,0.001273255],"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":[0.00002145811,0.000007012877,0.001240701,0.0002046996,0.00001797251,0.0000068762,0.00001938128,0.000122892,0.000009045105,0.0003767931,0.9963039,0.001669307],"study_design_scores_gemma":[0.000127154,0.00001142539,0.02558436,0.0006579644,0.00005447768,0.00002463866,0.0004156532,0.0004982272,0.0001787485,0.0005764184,0.9717999,0.00007101368],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006485214,0.00004303397,0.0000233534,0.00009478591,0.00002048375,0.0000111229,0.998753,0.00005170231,0.0009377941],"genre_scores_gemma":[0.0008865127,0.0002535726,0.0002999474,0.0001125067,0.00001527469,0.00009206036,0.9938591,0.00009426103,0.004386707],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09084006,"threshold_uncertainty_score":0.3157045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02577518568790341,"score_gpt":0.2373763416528362,"score_spread":0.2116011559649328,"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."}}