{"id":"W6939190936","doi":"10.6068/dp14ba86da8d240","title":"Trend 1994 - 1997. Statistics Canada. CANSIM: Construction - Machinery and Equipment | Country: Canada | Table: Capital and repair expenditures, industry sector 52, finance and insurance | Variable: Funds and other financial vehicles (x 1,000,000), Repair expenditures | Units: $CAD, 1994-1997. 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; Capital (architecture); Official statistics; Investment (military); Descriptive statistics; Summary statistics; Year-ending; Statistical analysis","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":[0.00186617,0.002294989,0.0024462,0.00827265,0.003293989,0.004338189,0.00486479,0.001372713,0.09283005],"category_scores_gemma":[0.01634958,0.001647514,0.001875712,0.03666074,0.0006162901,0.002427583,0.002188853,0.002949854,0.05186496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04942025,"about_ca_system_score_gemma":0.1277017,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948991,"about_ca_topic_score_gemma":0.9933119,"domain_scores_codex":[0.9959805,0.0002197484,0.0003900376,0.0005321579,0.001921,0.0009565547],"domain_scores_gemma":[0.9694891,0.0010048,0.0009720622,0.0008872285,0.02619776,0.001449097],"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":[0.00002453901,0.000007057629,0.001263052,0.0002399447,0.00002152321,0.00000809524,0.00002392226,0.0001213117,0.000009881805,0.0004333132,0.9960095,0.001837903],"study_design_scores_gemma":[0.0001304067,0.00001184295,0.02474324,0.000712166,0.00006264873,0.00002666327,0.0004602939,0.0004485,0.0001703135,0.0006125341,0.9725471,0.00007440906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006760369,0.00005244235,0.00002863138,0.0001173826,0.00002503062,0.00001374171,0.9986019,0.0000553112,0.001037884],"genre_scores_gemma":[0.001067845,0.0002877888,0.0004046356,0.0001368574,0.0000166065,0.0001118577,0.9933147,0.0001060393,0.004553692],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09283005,"threshold_uncertainty_score":0.3585707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864004303769147,"score_gpt":0.2353639347730026,"score_spread":0.2167238917353111,"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."}}