{"id":"W6957842672","doi":"10.6068/dp14ba8c34d2c8","title":"Trend 2007 - 2012. Statistics Canada. CANSIM: Business, Consumer and Property Services - Rental and Leasing and Real Estate | Country: Canada | Table: Commercial and industrial machinery and equipment rental and leasing, operating expenses, by North American Industry Classification System (NAICS) | Variable: Repair and maintenance, Commercial and industrial machinery and equipment rental and leasing | Units: %, 2007-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-014.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Renting; Real estate; Census; Official statistics; Summary statistics; Real property; Goods and services; Property management","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":[0.001579241,0.00267863,0.002846854,0.00884804,0.002787079,0.005010455,0.005112865,0.001529911,0.06650973],"category_scores_gemma":[0.01542332,0.001557936,0.00186921,0.04254047,0.0006734867,0.002556908,0.002147963,0.003272735,0.06072731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03599793,"about_ca_system_score_gemma":0.09291293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9887547,"about_ca_topic_score_gemma":0.9876172,"domain_scores_codex":[0.996325,0.0002080654,0.0003547975,0.0005834777,0.001622638,0.000905999],"domain_scores_gemma":[0.9718233,0.001034023,0.0009541126,0.0009294322,0.0239741,0.001284946],"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":[0.00001769217,0.000005744944,0.000945884,0.0001488399,0.00001565301,0.00000562565,0.00001387332,0.00009263359,0.00000814663,0.0002260165,0.9975501,0.0009698076],"study_design_scores_gemma":[0.0001429139,0.00001107319,0.02218364,0.0006577216,0.00005617775,0.00002621313,0.0004550841,0.0005427881,0.0001972581,0.0005563505,0.9750988,0.00007203824],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005449863,0.00003862428,0.00001460272,0.00007615786,0.00001934603,0.00000671567,0.9992613,0.00004558271,0.0004830165],"genre_scores_gemma":[0.000416232,0.0001157732,0.0001340001,0.00006090854,0.00001111063,0.00004558656,0.9973729,0.00004777075,0.001795785],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06650973,"threshold_uncertainty_score":0.2611845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03639566766364005,"score_gpt":0.246083863019143,"score_spread":0.209688195355503,"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."}}