{"id":"W6939338810","doi":"10.6068/dp14ba8e165b283","title":"Trend 1997 - 2010. Statistics Canada. CANSIM: Government - Employment and Remuneration | Country: Canada | Table: Labour statistics by business sector industry and non-commercial activity, consistent with the System of National Accounts, by North American Industry Classification System (NAICS) | Variable: Hours worked for all jobs, Publishing industries, information services and data processing services, Business sector | Units: Hours x 1,000, 1997-2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-104.","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; Official statistics; Remuneration; Government (linguistics); Census; Public sector; Social statistics; Private sector; Wages and salaries; National accounts","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":[0.002040782,0.002411647,0.002771856,0.00882246,0.003366705,0.004666223,0.005131187,0.001436699,0.07876912],"category_scores_gemma":[0.01737651,0.001702167,0.002026164,0.04203713,0.0006379992,0.00241484,0.002255998,0.003238128,0.05046004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0538231,"about_ca_system_score_gemma":0.1412771,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950478,"about_ca_topic_score_gemma":0.9937013,"domain_scores_codex":[0.99569,0.0002791062,0.0004464658,0.0005559784,0.002053257,0.0009751068],"domain_scores_gemma":[0.9639521,0.001103973,0.001022458,0.0009144639,0.0314585,0.001548419],"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":[0.00002361886,0.00000634587,0.0009623601,0.0002297286,0.00002200575,0.000006710839,0.00002046867,0.00009427439,0.000008423694,0.0003264105,0.996824,0.001475649],"study_design_scores_gemma":[0.0001641141,0.00001348651,0.02799048,0.0009037125,0.00008183039,0.00003050994,0.0005428722,0.000504719,0.0001887026,0.0006610822,0.9688259,0.00009271625],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005993435,0.00005310467,0.00002173883,0.0001235714,0.00002936197,0.00001341021,0.9988961,0.00005244585,0.0007501786],"genre_scores_gemma":[0.0008485084,0.0002817816,0.0003340861,0.0001561362,0.00001961075,0.0001079953,0.9942214,0.00009446983,0.00393596],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07876912,"threshold_uncertainty_score":0.3905157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054565976285704,"score_gpt":0.2369328366688373,"score_spread":0.2063871769059802,"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."}}