{"id":"W6901479871","doi":"10.6068/dp14ba8bb594f66","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: Annual average number of hours worked for all jobs, Machinery manufacturing, Business sector | Units: , 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); Census; Remuneration; Public sector; Social statistics; Wages and salaries; National accounts; Private sector","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":[0.002222216,0.002379625,0.002720203,0.008654378,0.003488308,0.004877458,0.004922202,0.001458453,0.09041968],"category_scores_gemma":[0.01878464,0.001752214,0.002008855,0.04056376,0.0006068381,0.002630766,0.002316812,0.003205188,0.05904777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05111049,"about_ca_system_score_gemma":0.1355878,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9938285,"about_ca_topic_score_gemma":0.9920454,"domain_scores_codex":[0.9953915,0.0003202852,0.0004791933,0.000581126,0.002205073,0.001022945],"domain_scores_gemma":[0.9625537,0.00122938,0.001014711,0.001031061,0.03266515,0.001506105],"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":[0.00002132872,0.000006133761,0.0008355696,0.000215336,0.00001919664,0.00000678904,0.00001964283,0.00009267671,0.000008176043,0.0003509529,0.9968396,0.001584623],"study_design_scores_gemma":[0.0001298175,0.00001128397,0.02120939,0.0008021665,0.00006502821,0.00002777769,0.0004497558,0.0004221512,0.0001681124,0.0006552867,0.9759732,0.00008592135],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005320992,0.00005188015,0.00002524569,0.0001303301,0.00003095805,0.00001433603,0.9987553,0.00005755587,0.0008811924],"genre_scores_gemma":[0.0007578303,0.0002842237,0.0003785233,0.0001606759,0.00001996683,0.0001121171,0.9939188,0.0001169106,0.004250877],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09041968,"threshold_uncertainty_score":0.3708343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147394193979307,"score_gpt":0.2405037087641028,"score_spread":0.2190297668243097,"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."}}