{"id":"W6976991555","doi":"10.6068/dp14ba854cb515","title":"Trend 1998 - 2010. 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: Oil pipelines (x 1,000,000), Mining and oil and gas extraction | Units: $CAD, 1998-2010. 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; Stock (firearms); Summary statistics; Index (typography); Asset (computer security); Capital (architecture); 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":[0.001396134,0.002402846,0.002353008,0.008367371,0.002767531,0.004408378,0.004738932,0.00130153,0.07395604],"category_scores_gemma":[0.01287364,0.00158254,0.001852563,0.03819691,0.0005627681,0.002453045,0.002025709,0.002660184,0.04723612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04511611,"about_ca_system_score_gemma":0.09896326,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930086,"about_ca_topic_score_gemma":0.992433,"domain_scores_codex":[0.9967073,0.0001565609,0.0003230206,0.0004516173,0.001606017,0.0007555389],"domain_scores_gemma":[0.9734072,0.0007714065,0.0009224003,0.0007241961,0.02301439,0.001160417],"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":[0.00002477722,0.000007358657,0.001443323,0.0002175924,0.00002092822,0.000007611501,0.00002025203,0.0001356411,0.000009279646,0.0003758954,0.9961854,0.001551851],"study_design_scores_gemma":[0.0001364986,0.00001234298,0.02998176,0.000676055,0.00006160401,0.00002757836,0.0004578971,0.0005618149,0.0002102815,0.0005460239,0.9672523,0.00007585362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007082628,0.00004203328,0.00001872062,0.00008204005,0.000017849,0.00000964775,0.998951,0.00004557585,0.0007623437],"genre_scores_gemma":[0.0008003542,0.0002121023,0.0002202131,0.00008057208,0.00001236672,0.00007191287,0.9952591,0.00006759726,0.003275719],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07395604,"threshold_uncertainty_score":0.3273419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637260546215588,"score_gpt":0.2497725570311624,"score_spread":0.2233999515690065,"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."}}