{"id":"W6957542051","doi":"10.6068/dp14ba86b378a14","title":"Trend 1998 - 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: Irrigation and land reclamation projects (x 1,000,000), Retail trade | Units: $CAD, 1998-2011. 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); Index (typography); Descriptive statistics; Capital (architecture)","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":[0.001454782,0.002322837,0.002288216,0.008046273,0.002791155,0.004396342,0.00460354,0.001297795,0.08229706],"category_scores_gemma":[0.01308889,0.001607566,0.001791965,0.03714843,0.0005501146,0.002440833,0.002042389,0.002655039,0.05025357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04563682,"about_ca_system_score_gemma":0.1003408,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930074,"about_ca_topic_score_gemma":0.9922408,"domain_scores_codex":[0.9965333,0.0001721779,0.0003346846,0.0004532483,0.001722072,0.0007846028],"domain_scores_gemma":[0.9721243,0.0008298335,0.0009136499,0.0007463311,0.0242218,0.00116408],"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":[0.00002296155,0.000007542726,0.001395494,0.0002230317,0.00001966971,0.00000775752,0.00002192902,0.0001409685,0.000009293129,0.0004097098,0.9959986,0.001743029],"study_design_scores_gemma":[0.0001194276,0.0000111957,0.02737539,0.0006554892,0.00005474149,0.00002555755,0.0004491236,0.0005220685,0.0001915696,0.0005459671,0.9699781,0.00007142646],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007086367,0.00004418523,0.00002226452,0.00008960724,0.00001908958,0.00001083113,0.9987902,0.000049141,0.0009039397],"genre_scores_gemma":[0.0008620008,0.0002432069,0.0002711423,0.00009059802,0.00001327344,0.0000831639,0.9943665,0.00008293691,0.003987283],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08229706,"threshold_uncertainty_score":0.3311198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02926727950936312,"score_gpt":0.2392340700511301,"score_spread":0.209966790541767,"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."}}