{"id":"W6957583810","doi":"10.6068/dp14ba8c69a5157","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: Geometric (infinite) end-year net stock, Furniture (x 1,000,000), Information and cultural industries, | Units: $CAD Chained (2007) $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; Summary statistics; Official statistics; Stock (firearms); Index (typography); Investment (military); Publication; 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":[0.001088254,0.00204193,0.00215385,0.007811667,0.002360067,0.003823744,0.004055468,0.001247763,0.0746271],"category_scores_gemma":[0.01349887,0.001314847,0.001689103,0.03646973,0.0005345754,0.002054222,0.001914524,0.002438467,0.04741443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03471171,"about_ca_system_score_gemma":0.08559605,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9914824,"about_ca_topic_score_gemma":0.9900101,"domain_scores_codex":[0.9974637,0.0001193343,0.0002722601,0.0003826933,0.001152876,0.0006090282],"domain_scores_gemma":[0.9775085,0.0008708973,0.0008699584,0.0006726632,0.01903637,0.001041532],"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":[0.00002159224,0.000006813014,0.001420082,0.0002060062,0.00002010587,0.000007583119,0.00001735374,0.00013175,0.000008928436,0.0003501012,0.9964483,0.001361374],"study_design_scores_gemma":[0.0001559267,0.00001189743,0.02993533,0.000742718,0.00006441529,0.00002953,0.0004389104,0.0005868436,0.0002093205,0.0006495147,0.9671035,0.00007192606],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000679691,0.00003854244,0.00001617436,0.00007012246,0.00001540445,0.000007329338,0.9991375,0.00004122149,0.0006056336],"genre_scores_gemma":[0.0007362003,0.0001741866,0.0001682108,0.00007466813,0.00001171941,0.00005256561,0.9961181,0.00004921885,0.002615051],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0746271,"threshold_uncertainty_score":0.2518523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751098086128034,"score_gpt":0.2274521680500665,"score_spread":0.2099411871887862,"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."}}