{"id":"W6957596571","doi":"10.6068/dp14ba899301454","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: Passenger terminals (x 1,000,000), Professional, scientific and technical 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); Index (typography); Descriptive statistics; Publication","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":[0.001424632,0.002317946,0.00228367,0.008264548,0.002765495,0.004602338,0.004419964,0.001372938,0.07934439],"category_scores_gemma":[0.0136532,0.001564299,0.001733798,0.03629867,0.0005535954,0.002377248,0.001994822,0.002696319,0.05156069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04454742,"about_ca_system_score_gemma":0.09467557,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9920366,"about_ca_topic_score_gemma":0.9913823,"domain_scores_codex":[0.9967028,0.0001690727,0.0003232391,0.0004473831,0.001610618,0.0007468978],"domain_scores_gemma":[0.9729626,0.0009113462,0.0009265652,0.0007728972,0.02324035,0.001186125],"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":[0.00001998358,0.000006548661,0.001239986,0.0001987438,0.00001726853,0.000006984735,0.00001844363,0.0001330119,0.00000774762,0.0003519012,0.9965088,0.001490642],"study_design_scores_gemma":[0.0001144162,0.00001022664,0.02549763,0.000669315,0.00005125345,0.00002536744,0.0004197517,0.0005412868,0.0001813189,0.000535175,0.9718827,0.00007155163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006386173,0.00004097985,0.0000195791,0.00008511978,0.00001733069,0.000008968284,0.9989729,0.00004661192,0.000744694],"genre_scores_gemma":[0.0007329444,0.0002117059,0.0002248132,0.00008240782,0.00001209712,0.00006922698,0.995381,0.00007060367,0.003215175],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07934439,"threshold_uncertainty_score":0.3232157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02744081924289847,"score_gpt":0.2658276762329247,"score_spread":0.2383868569900262,"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."}}