{"id":"W6976858497","doi":"10.6068/dp14ba8afaca512","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: End-year gross stock, Sewage engineering (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); Publication; Stock (firearms); Investment (military); Capital (architecture); Asset (computer security)","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":[0.001344216,0.002196587,0.002205187,0.00785554,0.002755866,0.004046987,0.004410666,0.001357448,0.06987831],"category_scores_gemma":[0.01409959,0.001384501,0.001763339,0.03507792,0.0005825034,0.002193684,0.001996296,0.002675903,0.04775022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03882506,"about_ca_system_score_gemma":0.09681317,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9928042,"about_ca_topic_score_gemma":0.9919426,"domain_scores_codex":[0.997109,0.0001457688,0.0002813562,0.000430561,0.00132618,0.0007072256],"domain_scores_gemma":[0.9760718,0.0008769686,0.0008641699,0.0007435929,0.02034584,0.00109761],"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":[0.00001921656,0.000006173588,0.001205297,0.0001884865,0.00001908037,0.000006865102,0.00001704495,0.0001245685,0.000009862985,0.0003456821,0.9967775,0.001280455],"study_design_scores_gemma":[0.000133958,0.0000101601,0.02472823,0.0006613875,0.00005894626,0.00002583227,0.0003928411,0.0005209304,0.0002131274,0.0006297107,0.9725514,0.00007355709],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005753515,0.00004068325,0.00001824345,0.00008062339,0.00001781623,0.000007891563,0.9991245,0.0000446885,0.0006080486],"genre_scores_gemma":[0.0006726272,0.0001760376,0.0002174038,0.00008533309,0.00001224211,0.00005588867,0.9960309,0.00005778828,0.002691776],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06987831,"threshold_uncertainty_score":0.2816969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473357967823634,"score_gpt":0.2242180997756906,"score_spread":0.2094845200974542,"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."}}