{"id":"W6957985427","doi":"10.6068/dp15e0091cee745","title":"Trend 1985 - 2011. Bureau of Labor Statistics. International Labor Statistics [Archive]: Manufacturing Labor Productivity and Unit Labor Costs | Country: South Korea | Seasonally Adjusted: Non-Seasonally Adjusted | Industry: Manufacturing | Series: AVERAGE ANNUAL COMPENSATION, NATIONAL CURRENCY BASIS, PRODUCTIVITY SERIES, 1985-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 002-031-005.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unit (ring theory); Productivity; Currency; Liberian dollar; Labor cost; National Income and Product Accounts; National accounts; Index (typography); Compensation of employees; Measures of national income and output","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.002462863,0.002507526,0.002707493,0.0008706602,0.000735666,0.0009270899,0.005414427,0.0014959,0.00986866],"category_scores_gemma":[0.0007021981,0.002612252,0.000006532854,0.0001670667,0.002113125,0.003404132,0.003271838,0.003768219,0.001581571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004805807,"about_ca_system_score_gemma":0.002977619,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1076174,"about_ca_topic_score_gemma":0.1182922,"domain_scores_codex":[0.9857239,0.001598738,0.002156641,0.004465471,0.0042618,0.001793467],"domain_scores_gemma":[0.9865981,0.001337872,0.004639641,0.005666218,0.0006684321,0.001089689],"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":[0.002049667,0.0009215443,0.002763214,0.002794732,0.001993417,0.000481527,0.00009818481,0.00007005339,0.00007286239,0.003856619,0.9839553,0.0009428461],"study_design_scores_gemma":[0.002977667,0.0003175633,0.023277,0.000344855,0.001002413,0.0004245734,0.0002719969,0.0008567844,0.00001571711,0.00002267684,0.9679888,0.002499968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001949147,0.001023769,0.00007548172,0.0000791048,0.001469028,0.002919988,0.9923964,0.0004361053,0.001405183],"genre_scores_gemma":[0.000924763,0.001244249,0.004913338,0.00008932131,0.001896109,0.0001451303,0.9836727,0.0009480485,0.006166307],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02051379,"threshold_uncertainty_score":0.9999667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03545146890722766,"score_gpt":0.2897196755046176,"score_spread":0.25426820659739,"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."}}