{"id":"W6901541110","doi":"10.6068/dp14ba8c53bae16","title":"Trend 1997 - 2011. 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, Other professional, scientific and technical services, Business sector | Units: Hours x 1,000, 1997-2011. 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; Government (linguistics); Remuneration; Census; Public sector; Wages and salaries; Social statistics; Private sector; 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":[0.002088216,0.002386512,0.002778523,0.008593696,0.003288832,0.004635978,0.005148572,0.001447294,0.08707786],"category_scores_gemma":[0.017566,0.001738844,0.001962403,0.04166769,0.0006122187,0.002512197,0.002212458,0.003262972,0.05717888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04989128,"about_ca_system_score_gemma":0.1309109,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940084,"about_ca_topic_score_gemma":0.9920827,"domain_scores_codex":[0.9956822,0.0002864903,0.0004482452,0.0005575395,0.002065031,0.0009604396],"domain_scores_gemma":[0.9634346,0.001209299,0.001010176,0.0009609279,0.0318599,0.001524916],"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":[0.00002109193,0.000006120027,0.0008412644,0.0002145357,0.00001896959,0.000006275433,0.00001947205,0.00008873235,0.000007942293,0.0003188179,0.9970181,0.001438727],"study_design_scores_gemma":[0.0001507939,0.00001177448,0.02401889,0.0008290317,0.00006933273,0.00002699941,0.0004862577,0.0004393636,0.0001738471,0.0006326115,0.973075,0.00008610431],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005200368,0.00004527621,0.00002129644,0.000113624,0.00002654594,0.00001287555,0.9989275,0.00005218482,0.0007486059],"genre_scores_gemma":[0.0007008171,0.000251401,0.0003182628,0.0001416577,0.00001805127,0.000107141,0.9944349,0.00009997102,0.003927743],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08707786,"threshold_uncertainty_score":0.3619882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834752060553782,"score_gpt":0.2514402557179853,"score_spread":0.2230927351124475,"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."}}