{"id":"W6939119115","doi":"10.6068/dp14ba8af17f551","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: Natural gas processing plants (x 1,000,000), Other services (except public administration) | 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":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","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":[0.001506643,0.002319857,0.00234349,0.008255873,0.002828853,0.004521833,0.004525924,0.001325108,0.08446244],"category_scores_gemma":[0.01415367,0.001649591,0.001780151,0.03786009,0.0005518434,0.002451992,0.002052649,0.002701553,0.05094225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04684095,"about_ca_system_score_gemma":0.1049136,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9931531,"about_ca_topic_score_gemma":0.9924781,"domain_scores_codex":[0.9963229,0.0001839651,0.0003644206,0.0004851414,0.001829478,0.0008141755],"domain_scores_gemma":[0.9699701,0.0009314171,0.001005819,0.0007989099,0.02603144,0.001262272],"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":[0.0000222268,0.000007158991,0.001365806,0.0002129611,0.00001989741,0.000007447269,0.00002082186,0.0001362054,0.000008706607,0.0004148187,0.9961696,0.001614412],"study_design_scores_gemma":[0.0001221162,0.00001157908,0.027697,0.0006499983,0.00005723862,0.00002638868,0.000444414,0.0005162862,0.0001872356,0.0005479619,0.9696662,0.0000736139],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007115452,0.00004404896,0.00002180785,0.00009481106,0.0000202061,0.00001075213,0.9987798,0.00004836789,0.0009090728],"genre_scores_gemma":[0.0008880171,0.0002465007,0.0002636465,0.00009755062,0.00001392273,0.00008319537,0.9941955,0.00008347122,0.004128149],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08446244,"threshold_uncertainty_score":0.3398565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02071315763977275,"score_gpt":0.2287292828249042,"score_spread":0.2080161251851314,"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."}}