{"id":"W6957380135","doi":"10.6068/dp14ba8d210d472","title":"Trend 2007 - 2012. Statistics Canada. CANSIM: Labor - Wages, Salaries and Other Earnings | Country: Canada | Table: Labour statistics consistent with the System of National Accounts (SNA), by province and territory, job category and North American Industry Classification System (NAICS) | Variable: Petroleum and coal products manufacturing (except petroleum refineries), Hours worked for all jobs | Units: Hours x 1,000, 2007-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-145.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Economic statistics; Census; Official statistics; Summary statistics; Wages and salaries; National accounts; Socioeconomic status; Immigration; Population statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001795751,0.002529021,0.002813207,0.008357067,0.002920884,0.004614027,0.005053415,0.001442812,0.08076247],"category_scores_gemma":[0.01548724,0.00174248,0.001901638,0.04188396,0.0005890038,0.002340118,0.002027496,0.003183977,0.05425292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05020782,"about_ca_system_score_gemma":0.1189995,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9941866,"about_ca_topic_score_gemma":0.9925836,"domain_scores_codex":[0.9959549,0.0002412794,0.0003877218,0.0005270796,0.00192316,0.0009659262],"domain_scores_gemma":[0.9679613,0.001033532,0.00106943,0.0008228202,0.02776013,0.001352773],"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":[0.00002294125,0.000007117319,0.0009723706,0.0001784806,0.00001891788,0.000005536602,0.00001580283,0.0001041862,0.00000740423,0.0002864528,0.9970883,0.001292559],"study_design_scores_gemma":[0.0001813557,0.00001399612,0.03113125,0.0007822399,0.00006606601,0.00002631968,0.0004586785,0.0006176383,0.0002126325,0.0006255927,0.9657907,0.00009344894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006103256,0.00004277011,0.00001748629,0.00009697735,0.00002358771,0.00001037039,0.999011,0.00005004793,0.0006866658],"genre_scores_gemma":[0.0007773771,0.0002081858,0.0002554359,0.0001199753,0.00001670814,0.00008316089,0.9947743,0.00008297677,0.003681999],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9192376,"threshold_uncertainty_score":0.3642849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908733435986968,"score_gpt":0.225486099061232,"score_spread":0.2063987647013623,"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."}}