{"id":"W6957715141","doi":"10.6068/dp14ba87c545f12","title":"Trend 1999 - 2011. Statistics Canada. CANSIM: Government - Government Business Enterprises | Country: Canada | Table: Balance sheet and income statement of federal government business enterprises, by North American Industry Classification System (NAICS), end of fiscal year closest to December 31 | Variable: Depreciation, depletion and amortization expense, Goods industries | Units: $CAD x 1,000, 1999-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-105.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Spatial Neglect and Hemispheric Dysfunction","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Economic statistics; Balance sheet; Income statement; Official statistics; Census; Goods and services; Payroll; Business statistics","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":[0.001656352,0.002490615,0.002581724,0.00917065,0.003325619,0.005215673,0.005030141,0.001526795,0.07779337],"category_scores_gemma":[0.01511142,0.001686157,0.001973373,0.04306597,0.0006185259,0.002650723,0.002139531,0.003095615,0.05963839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0474811,"about_ca_system_score_gemma":0.1182459,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9932536,"about_ca_topic_score_gemma":0.9919463,"domain_scores_codex":[0.996177,0.0001971377,0.0003935472,0.0004992864,0.001836519,0.0008964873],"domain_scores_gemma":[0.9680802,0.000949726,0.001024486,0.0009185347,0.0277598,0.001267345],"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":[0.00002018107,0.000006389089,0.0009008239,0.0001923502,0.00001658089,0.00000679899,0.00001577017,0.00009779474,0.000008398715,0.0003449815,0.9970343,0.001355651],"study_design_scores_gemma":[0.0001126655,0.000009777776,0.02134737,0.0006629702,0.00005463532,0.00002320133,0.0003940429,0.0004308333,0.0001841542,0.0005581094,0.9761496,0.0000727115],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005103605,0.00004621275,0.000017119,0.00009876289,0.00002320629,0.000009742975,0.9989222,0.00004635519,0.0007853242],"genre_scores_gemma":[0.0005887659,0.0002267617,0.0002189456,0.00009795776,0.0000143903,0.00006438536,0.9953342,0.00006659244,0.003387939],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07779337,"threshold_uncertainty_score":0.3445011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156911661649333,"score_gpt":0.2247507442686416,"score_spread":0.2090595781037083,"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."}}