{"id":"W6938866385","doi":"10.6068/dp14ba8cee2c330","title":"Trend 2001 - 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: 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: 075-001-036.","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; Official statistics; Asset (computer security); Stock (firearms); Capital (architecture); Index (typography); Summary statistics","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":[0.001481933,0.002259061,0.002241646,0.00844609,0.002804061,0.004491413,0.004494602,0.0013536,0.08252726],"category_scores_gemma":[0.01456209,0.001612001,0.001785498,0.03636433,0.0005575513,0.002274481,0.001978846,0.002759119,0.05259512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04268711,"about_ca_system_score_gemma":0.1050088,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9922701,"about_ca_topic_score_gemma":0.9912983,"domain_scores_codex":[0.9965359,0.0001747016,0.000352434,0.0004655775,0.001701646,0.0007697676],"domain_scores_gemma":[0.9704329,0.0009919883,0.0009809176,0.0008123164,0.02557918,0.001202797],"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":[0.00002015884,0.00000643282,0.00122566,0.0002138269,0.00001820066,0.000006870655,0.00001825312,0.0001256901,0.000008446997,0.000354823,0.9964855,0.001516071],"study_design_scores_gemma":[0.0001205743,0.00001050979,0.0250078,0.0006806036,0.00005594342,0.00002478959,0.0004093597,0.0004744803,0.0001821074,0.0005352976,0.9724298,0.00006882543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000604866,0.00004182598,0.0000197216,0.00008728937,0.00001924851,0.000009593956,0.9989375,0.00004567898,0.000778564],"genre_scores_gemma":[0.0007284072,0.0002216396,0.0002338449,0.00009437857,0.00001344527,0.00007488571,0.9948925,0.00007462706,0.003666324],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08252726,"threshold_uncertainty_score":0.3097181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02225446594923692,"score_gpt":0.2358471238045632,"score_spread":0.2135926578553263,"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."}}