{"id":"W6957513584","doi":"10.6068/dp14ba8b392bc33","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: Total non-residential building construction (x 1,000,000), Mining and oil and gas extraction | 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; Official statistics; Census; Stock (firearms); Summary statistics; Index (typography); Asset (computer security); 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":[0.001460543,0.00230238,0.002300297,0.008269588,0.002777103,0.004480744,0.004608887,0.001334628,0.07654522],"category_scores_gemma":[0.01416042,0.001566355,0.00181034,0.03798127,0.0005713701,0.002411158,0.002032923,0.00267288,0.04870614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04398836,"about_ca_system_score_gemma":0.09758807,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9926068,"about_ca_topic_score_gemma":0.9921076,"domain_scores_codex":[0.9966158,0.0001690549,0.0003349803,0.0004601914,0.001667285,0.0007525968],"domain_scores_gemma":[0.9711613,0.0009358462,0.0009907034,0.0008296224,0.02486165,0.001220839],"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":[0.00002302429,0.000007268887,0.001362158,0.0002184311,0.00002005766,0.000007549593,0.00002075892,0.0001415498,0.000009000924,0.0003955423,0.99628,0.001514614],"study_design_scores_gemma":[0.0001295045,0.0000115193,0.027326,0.0006581928,0.00005592697,0.0000255961,0.0004498077,0.0005440948,0.0001991742,0.0005575916,0.9699665,0.00007611894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006689527,0.00003893648,0.00001923145,0.00008135639,0.00001700005,0.000009823842,0.9989741,0.00004535586,0.0007474288],"genre_scores_gemma":[0.0007558704,0.0001997392,0.0002337095,0.00007894865,0.0000116811,0.00007402858,0.9953584,0.0000692186,0.003218389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07654522,"threshold_uncertainty_score":0.3191594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038714128342807,"score_gpt":0.248987054208765,"score_spread":0.2285999129253369,"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."}}