{"id":"W6976692053","doi":"10.6068/dp14ba7cb622723","title":"Trend 2001 - 2011. Statistics Canada. CANSIM: Construction - Residential Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Aircraft hangars (x 1,000,000), Accommodation and food services | Units: $CAD, 2001-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-037.","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; Stock (firearms); Asset (computer security); Accommodation; Index (typography); Descriptive statistics","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":[0.001538765,0.002356014,0.002360072,0.008293492,0.002783202,0.004428456,0.004536754,0.001387122,0.08436682],"category_scores_gemma":[0.0141905,0.001641856,0.001788563,0.03752094,0.0005352012,0.002386718,0.002011064,0.002744386,0.05172855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04523027,"about_ca_system_score_gemma":0.09747934,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929479,"about_ca_topic_score_gemma":0.9917482,"domain_scores_codex":[0.9964818,0.0001843915,0.0003524764,0.0004608761,0.001708422,0.0008120862],"domain_scores_gemma":[0.9718222,0.000945112,0.0009643355,0.000791966,0.02421634,0.001260077],"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":[0.00002157059,0.000006839646,0.001302149,0.0002116891,0.00001815044,0.000007161792,0.00001964034,0.0001312764,0.000007967247,0.0003500435,0.9962991,0.001624373],"study_design_scores_gemma":[0.0001235199,0.00001188616,0.02901623,0.0007089979,0.00005690122,0.00002694633,0.0004543333,0.0005478461,0.0001829952,0.0005386085,0.9682559,0.00007572925],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006658115,0.00004111925,0.00002087631,0.00008682113,0.00001848044,0.000009943868,0.998925,0.00004801824,0.0007831127],"genre_scores_gemma":[0.0007996392,0.0002263457,0.0002540879,0.00009296389,0.00001324616,0.00008017979,0.9947449,0.00008076303,0.003707881],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08436682,"threshold_uncertainty_score":0.3281701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587946736816655,"score_gpt":0.2432270452133753,"score_spread":0.2173475778452088,"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."}}