{"id":"W6920354371","doi":"10.6068/dp14ba8b02a6b66","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, Furniture and related product manufacturing, 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; 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":[0.001959961,0.002396388,0.002749239,0.008263274,0.003182656,0.00449335,0.005135078,0.00142618,0.08599396],"category_scores_gemma":[0.01649363,0.001705046,0.001936244,0.04078849,0.0006003484,0.002485547,0.0021738,0.003162195,0.058011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04707253,"about_ca_system_score_gemma":0.1202564,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936698,"about_ca_topic_score_gemma":0.9917759,"domain_scores_codex":[0.9959671,0.000266534,0.0004208272,0.0005385512,0.001888394,0.0009186559],"domain_scores_gemma":[0.9666497,0.001107097,0.0009558812,0.0009110932,0.02896588,0.001410341],"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":[0.00002100768,0.000006043361,0.0008450219,0.0002107978,0.00001861762,0.000006300868,0.00001857854,0.00008858147,0.00000787782,0.000304535,0.9970765,0.001396157],"study_design_scores_gemma":[0.0001502671,0.00001172011,0.02381827,0.0008019781,0.00006729673,0.00002737614,0.0004764711,0.000443113,0.000172625,0.0006163654,0.9733309,0.00008364772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000495981,0.0000420271,0.00001968291,0.0001019225,0.0000238728,0.00001148755,0.9990203,0.00004886796,0.0006821496],"genre_scores_gemma":[0.0006457061,0.0002263685,0.0002805036,0.0001253332,0.00001602212,0.00009412276,0.9950396,0.00008949288,0.003482802],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08599396,"threshold_uncertainty_score":0.3415367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02305426029226477,"score_gpt":0.2307067518176086,"score_spread":0.2076524915253438,"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."}}