{"id":"W6901825591","doi":"10.6068/dp15e00749f6828","title":"Trend 1996 - 2011. Bureau of Labor Statistics. International Labor Statistics [Archive]: Hourly Compensation of Manufacturing Workers | Country: Canada | Seasonally Adjusted: Non-Seasonally Adjusted | Industry: Manufacturing | Series: MANUFACTURING HR COMPENSATION INDEX, U.S. DOLLAR BASIS (YEAR 2000=100), 1996-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 002-031-004.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Payroll; Compensation of employees; Liberian dollar; Wage; Manufacturing; Production (economics); Apprenticeship; Wages and salaries; Guard (computer science)","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":[0.001669257,0.001967926,0.002047605,0.005170387,0.001663298,0.003578393,0.003742443,0.001396047,0.07676857],"category_scores_gemma":[0.01447695,0.001218373,0.001328707,0.02218837,0.0003988595,0.002206806,0.00178915,0.002935657,0.09190231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009572008,"about_ca_system_score_gemma":0.02157901,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8140056,"about_ca_topic_score_gemma":0.7853882,"domain_scores_codex":[0.9974995,0.0002063233,0.0003256917,0.0004949519,0.0009538609,0.0005196042],"domain_scores_gemma":[0.984907,0.0008507505,0.0007858733,0.0008984358,0.0118714,0.0006865009],"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":[0.00001689452,0.000006168309,0.0006668954,0.0001258397,0.00001040573,0.00000385485,0.000009801866,0.00005971485,0.000006867675,0.0001575323,0.9979673,0.0009687211],"study_design_scores_gemma":[0.000143496,0.00001160473,0.01790868,0.0006290429,0.00003561322,0.00001979252,0.000270584,0.0002460901,0.0001230504,0.0004953598,0.9800735,0.00004326235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003462264,0.00002128456,0.00001366529,0.00004931087,0.00002018051,0.000006567769,0.9994623,0.00003067925,0.0003613119],"genre_scores_gemma":[0.0002101826,0.00006121581,0.00009317878,0.00004032627,0.00001025146,0.00005225062,0.998464,0.00003134294,0.001037271],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9232314,"threshold_uncertainty_score":0.3741795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03061838722287102,"score_gpt":0.2723174154689689,"score_spread":0.2416990282460978,"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."}}