{"id":"W6976986293","doi":"10.6068/dp14ba86f117370","title":"Trend 2001 - 2011. Statistics Canada. CANSIM: Construction - Nonresidential Building Construction | Country: Canada | Table: Capital expenditures on construction, by type of asset and North American Industry Classification System (NAICS) sector | Variable: Other commercial construction (x 1,000,000), Construction | Units: $CAD, 2001-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-035.","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); Capital (architecture); Publication; Index (typography)","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":[0.001498307,0.002328546,0.002329066,0.008147481,0.002894311,0.004479477,0.00447283,0.001340138,0.08356992],"category_scores_gemma":[0.01345809,0.001600432,0.001775077,0.03573168,0.0005523107,0.002288127,0.002008818,0.002713603,0.05073932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04478532,"about_ca_system_score_gemma":0.1031042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932376,"about_ca_topic_score_gemma":0.9924316,"domain_scores_codex":[0.996515,0.0001832008,0.0003284564,0.0004603215,0.001689695,0.0008232711],"domain_scores_gemma":[0.9729605,0.0008819366,0.0008995705,0.0007564366,0.0232227,0.001278737],"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":[0.00002288371,0.000007201826,0.001382884,0.0002127035,0.00001905676,0.000007612116,0.00002083456,0.0001356404,0.000008882659,0.0003739771,0.9960721,0.001736409],"study_design_scores_gemma":[0.0001215447,0.0000115728,0.02814103,0.0006628522,0.00005461594,0.00002656321,0.0004505126,0.0005305486,0.0001806422,0.0005401234,0.9692072,0.00007280579],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007145622,0.00004392201,0.00002304892,0.0000924133,0.00001938136,0.00001093328,0.9988175,0.00005239818,0.0008688541],"genre_scores_gemma":[0.0008791144,0.0002358343,0.0002854581,0.00009826764,0.00001401281,0.00008473737,0.9943529,0.00008526915,0.003964324],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08356992,"threshold_uncertainty_score":0.3249418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131779017481465,"score_gpt":0.2591752060322177,"score_spread":0.227857415857403,"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."}}