{"id":"W6920190334","doi":"10.6068/dp14ba894aa6268","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: Total compensation per job, Electronic product 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); Remuneration; Census; Wages and salaries; Public sector; 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":[0.00211495,0.002327016,0.002612929,0.008648445,0.003556803,0.005025185,0.004925076,0.001500336,0.09008703],"category_scores_gemma":[0.01858166,0.001722913,0.001925351,0.03984012,0.0006351216,0.002656084,0.002360272,0.003222132,0.06029018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04839704,"about_ca_system_score_gemma":0.1282358,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9933814,"about_ca_topic_score_gemma":0.9916645,"domain_scores_codex":[0.9956096,0.000293539,0.0004493835,0.0005688948,0.002072374,0.001006309],"domain_scores_gemma":[0.9644291,0.001217385,0.0009760034,0.001038547,0.03081294,0.001526084],"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":[0.00001978698,0.000005660464,0.0007946813,0.0001882527,0.0000172138,0.00000638347,0.00001849636,0.00008360779,0.000007639985,0.0003273124,0.9971469,0.001383988],"study_design_scores_gemma":[0.0001343706,0.0000109858,0.02036771,0.0007748494,0.00006054715,0.00002836697,0.000450057,0.0004199738,0.0001712223,0.0006709477,0.9768281,0.00008280211],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004918239,0.00004298471,0.00002208857,0.0001161359,0.00002775568,0.00001278969,0.9988728,0.0000537862,0.0008023928],"genre_scores_gemma":[0.0006683078,0.0002307494,0.000321033,0.0001390863,0.0000180535,0.00009891677,0.994812,0.0001036309,0.003608224],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09008703,"threshold_uncertainty_score":0.3511468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966035079952063,"score_gpt":0.2235290411976744,"score_spread":0.2038686903981538,"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."}}