{"id":"W6901580456","doi":"10.6068/dp14ba8cc2a4e42","title":"Trend 1961 - 2013. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Flows and stocks of fixed non-residential capital, by sector of North American Industry Classification System (NAICS) and asset | Variable: Straight-line end-year net stock, Other transportation (x 1,000,000), Manufacturing, 2007 constant prices | Units: $CAD, 1961-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-034.","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; Index (typography); Stock (firearms); Investment (military); Publication; 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":[0.00139707,0.002144611,0.002181288,0.008117925,0.002691125,0.004002429,0.004384817,0.001339018,0.07381576],"category_scores_gemma":[0.01507845,0.001404415,0.001777572,0.03718261,0.0005660006,0.002224774,0.001987141,0.002682314,0.04601084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03964324,"about_ca_system_score_gemma":0.1009302,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929262,"about_ca_topic_score_gemma":0.9913658,"domain_scores_codex":[0.9969596,0.0001506641,0.0003006543,0.0004276271,0.001424911,0.0007366652],"domain_scores_gemma":[0.9732896,0.000960675,0.0009036575,0.000754524,0.02292926,0.001162338],"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":[0.00001924048,0.00000632373,0.001198453,0.0001927579,0.00001867581,0.000006929446,0.00001727258,0.0001256508,0.000009232448,0.0003782832,0.9966617,0.001365594],"study_design_scores_gemma":[0.0001374473,0.00001090354,0.02637256,0.0006926681,0.00006120713,0.00002672731,0.0004009182,0.0005216983,0.000212286,0.0006509729,0.9708378,0.00007474358],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005861607,0.00003989013,0.00001904708,0.00008608662,0.0000187818,0.000008705426,0.9990483,0.00004564006,0.0006748824],"genre_scores_gemma":[0.0007429891,0.0001937527,0.0002380551,0.00009531484,0.00001349961,0.00006373312,0.9955379,0.00006427012,0.003050597],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07381576,"threshold_uncertainty_score":0.2876332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777653214328898,"score_gpt":0.2347229902330399,"score_spread":0.2169464580897509,"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."}}