{"id":"W6957657829","doi":"10.6068/dp14ba7c3db1069","title":"Trend 1994 - 2011. Statistics Canada. CANSIM: Construction - Nonresidential Engineering Construction | Country: Canada | Table: Capital and repair expenditures, industry sector 52, finance and insurance | Variable: Credit intermediation and related activities (x 1,000,000), Capital and repair expenditures | Units: $CAD, 1994-2011. 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; Descriptive statistics; Capital (architecture); Census; Statistician; Private sector; Summary statistics; Intermediation; Official statistics; Value (mathematics)","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":[0.001704167,0.002428053,0.002404152,0.008334659,0.003288084,0.004726525,0.004856782,0.001447212,0.09102497],"category_scores_gemma":[0.01549562,0.001645428,0.001943895,0.03686908,0.0006109258,0.002546967,0.002263966,0.002947746,0.05314438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04838933,"about_ca_system_score_gemma":0.1172902,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941015,"about_ca_topic_score_gemma":0.9929293,"domain_scores_codex":[0.9961421,0.0001987679,0.0003749994,0.0005197451,0.001848655,0.0009156682],"domain_scores_gemma":[0.9700224,0.000949673,0.0009330525,0.0008750355,0.0258594,0.00136048],"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":[0.00002362307,0.000007055954,0.001264912,0.0002417474,0.00002035873,0.000007677061,0.00002252421,0.0001261809,0.00001039742,0.0004092174,0.9959365,0.00192982],"study_design_scores_gemma":[0.0001179521,0.00001085609,0.02328449,0.0007481269,0.00006113775,0.00002492106,0.0004249203,0.0004578824,0.0001779212,0.0006074866,0.9740081,0.00007612663],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006239134,0.00005111968,0.00002683702,0.0001037812,0.00002285246,0.0000120586,0.9987354,0.00005371263,0.0009319735],"genre_scores_gemma":[0.0009059347,0.0002767904,0.0003675238,0.0001222951,0.00001590331,0.000101579,0.9940795,0.00009955165,0.00403101],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09102497,"threshold_uncertainty_score":0.3510908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009972784548185051,"score_gpt":0.2099485007004228,"score_spread":0.1999757161522377,"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."}}