{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008512199,0.00089075,0.001166125,0.00011627,0.0007643914,0.001335357,0.0003298836,0.0004963552,0.00007505771],"category_scores_gemma":[0.00007278579,0.0007278094,5.105915e-7,0.0001922207,0.001565159,0.0006685617,0.001448861,0.0009442302,4.602245e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001782268,"about_ca_system_score_gemma":0.001739804,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9903135,"about_ca_topic_score_gemma":0.9398668,"domain_scores_codex":[0.9953707,0.0005003337,0.001035316,0.001675757,0.0006512594,0.0007666417],"domain_scores_gemma":[0.9972738,0.0003485548,0.0007509813,0.0007093492,0.00008474896,0.0008325519],"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":[0.0004167012,0.00005159976,0.005668104,0.0009404772,0.0001088969,0.00008131026,0.00004247921,7.24456e-7,0.0001553339,0.00006111157,0.9848079,0.007665377],"study_design_scores_gemma":[0.002493966,0.0002032863,0.0009264338,0.0003993521,0.0005262257,0.001122884,0.002008475,0.005869903,3.63394e-7,1.877607e-7,0.9855102,0.0009387063],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02393194,0.00721896,0.000004159855,0.00003931702,0.0004231936,0.001234516,0.9667159,0.00006513934,0.0003669054],"genre_scores_gemma":[0.009800264,0.01060866,0.0004054729,0.0001804238,0.0003994864,0.00003973495,0.9780746,0.0001404956,0.0003508964],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05044673,"threshold_uncertainty_score":0.9997014,"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."}}