{"id":"W6957712380","doi":"10.6068/dp14ba885c61560","title":"Trend 2007 - 2011. Statistics Canada. CANSIM: Business, Consumer and Property Services - Professional, Scientific and Technical Services | Country: Canada | Table: Architectural services, operating expenses, by North American Industry Classification System (NAICS) | Variable: Rental and leasing, Landscape architectural services | Units: %, 2007-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-013.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Renting; Service (business); Publication; Summary statistics; Descriptive statistics; Financial services","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":[0.001970118,0.002499094,0.002766524,0.01010149,0.003516885,0.005676385,0.005323813,0.001725592,0.103512],"category_scores_gemma":[0.02123203,0.001722306,0.00211714,0.04843746,0.000711786,0.003165371,0.002526482,0.00328571,0.07619136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04984677,"about_ca_system_score_gemma":0.1353768,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9912421,"about_ca_topic_score_gemma":0.989185,"domain_scores_codex":[0.9953495,0.0002610454,0.0005048494,0.0006313959,0.002178022,0.001075238],"domain_scores_gemma":[0.9597038,0.001463646,0.001145401,0.001227502,0.03480595,0.00165368],"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":[0.00001499118,0.000004024417,0.0006517303,0.000180798,0.00001312432,0.000005106677,0.00001436082,0.00007554009,0.000007364563,0.0002979224,0.9976393,0.001095779],"study_design_scores_gemma":[0.0001022646,0.000008313717,0.01429658,0.0007745351,0.0000514782,0.00002363497,0.0003990803,0.0003530308,0.0001490118,0.0006162307,0.9831571,0.00006882261],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003716013,0.00004342484,0.00001999203,0.0001112078,0.00002708642,0.00001098655,0.9989339,0.00004945476,0.0007668277],"genre_scores_gemma":[0.0005194856,0.0002271918,0.000266384,0.0001298472,0.00001791138,0.00009679881,0.9954046,0.00009701044,0.003240734],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.103512,"threshold_uncertainty_score":0.3616653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919755264046718,"score_gpt":0.2439230083624661,"score_spread":0.2247254557219989,"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."}}