{"id":"W6920221804","doi":"10.6068/dp14ba8ef99aa90","title":"Trend 1961 - 2013. Statistics Canada. CANSIM: Construction - Nonresidential Engineering Construction | Country: Canada | Table: Flows and stocks of fixed non-residential capital, by sector of North American Industry Classification System (NAICS) and asset | Variable: Hyperbolic (delayed) end-year net stock, Intellectual property products (x 1,000,000), Business sector, Current prices | Units: $CAD, 1961-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-036.","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; Descriptive statistics; Stock (firearms); Business statistics; Official statistics; Index (typography); Summary 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":[0.00123887,0.002228666,0.002200576,0.008543684,0.002705906,0.004185528,0.00450972,0.001353897,0.07781928],"category_scores_gemma":[0.01456257,0.001443356,0.001717625,0.03752282,0.0005813301,0.002199076,0.002065973,0.002623314,0.04797105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0406501,"about_ca_system_score_gemma":0.09606496,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9927678,"about_ca_topic_score_gemma":0.991926,"domain_scores_codex":[0.9970741,0.0001359638,0.0002965094,0.0004328342,0.00135855,0.0007018922],"domain_scores_gemma":[0.9734386,0.0009548503,0.0009697571,0.0008189977,0.02264212,0.001175593],"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":[0.00002014739,0.000006395135,0.00129946,0.0002132634,0.00001897719,0.000006854216,0.00001905829,0.000136543,0.00000978625,0.0004133328,0.9964011,0.001455085],"study_design_scores_gemma":[0.0001289178,0.00001022332,0.02521186,0.0007079596,0.00005793965,0.00002514228,0.0004082845,0.00052482,0.0001996345,0.0006727778,0.9719804,0.00007194784],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005755997,0.00003948071,0.00001826809,0.00007109687,0.00001620957,0.000007593892,0.9990809,0.00004482087,0.0006640432],"genre_scores_gemma":[0.0007910873,0.0001935612,0.0002186288,0.00008310314,0.00001184877,0.00006257812,0.9956717,0.00006216711,0.002905276],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07781928,"threshold_uncertainty_score":0.2949385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207702844301544,"score_gpt":0.2240248659803767,"score_spread":0.2032545815502222,"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."}}