{"id":"W6901621804","doi":"10.6068/dp14ba8c7d27083","title":"Trend 2001 - 2011. Statistics Canada. CANSIM: Construction - Nonresidential Building Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Armouries, barracks and other similar military type structures (x 1,000,000), Construction | Units: $CAD, 2001-2011. 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; Summary statistics; Stock (firearms); Asset (computer security); Capital (architecture); Index (typography); Publication","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001423102,0.002302706,0.002257796,0.008439767,0.002852168,0.00442553,0.00442245,0.001317797,0.08160346],"category_scores_gemma":[0.01352098,0.001642331,0.001813546,0.03614684,0.0005455529,0.002206399,0.001967357,0.002718435,0.04877092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04496549,"about_ca_system_score_gemma":0.1055892,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933535,"about_ca_topic_score_gemma":0.9926834,"domain_scores_codex":[0.9965557,0.0001721299,0.0003431046,0.0004487069,0.001674767,0.0008056057],"domain_scores_gemma":[0.9727386,0.0008881942,0.0009575402,0.0007223961,0.02350038,0.001192818],"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":[0.00002122341,0.000006740268,0.001362481,0.0002176461,0.00001925087,0.000007328846,0.00001927284,0.0001315222,0.000008220209,0.0003579452,0.9962524,0.001596127],"study_design_scores_gemma":[0.0001280803,0.00001166007,0.0306378,0.0007099234,0.00006179405,0.00002775616,0.000449108,0.0005377618,0.0001934776,0.0005262236,0.9666435,0.00007297469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007011789,0.00004591984,0.0000200903,0.00009215029,0.00001954215,0.00001011219,0.9988686,0.00004676792,0.0008267133],"genre_scores_gemma":[0.0008602,0.0002477767,0.0002408449,0.00009633251,0.00001428497,0.00007679813,0.9943235,0.0000753296,0.004064872],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9183965,"threshold_uncertainty_score":0.326249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02661833559789451,"score_gpt":0.2522749716567899,"score_spread":0.2256566360588954,"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."}}