{"id":"W6958094235","doi":"10.6068/dp15e0092e8fc84","title":"Trend 1950 - 2011. Bureau of Labor Statistics. International Labor Statistics [Archive]: Manufacturing Labor Productivity and Unit Labor Costs | Country: France | Seasonally Adjusted: Non-Seasonally Adjusted | Industry: Manufacturing | Series: REAL AVERAGE ANNUAL COMPENSATION, CPI BASIS, PRODUCTIVITY SERIES, 1950-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 002-031-005.","year":2017,"lang":"en","type":"other","venue":"Data Planet","topic":"Particle Accelerators and Free-Electron Lasers","field":"Engineering","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; Real wages","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000711792,0.001194801,0.001415858,0.0002484559,0.0002914346,0.0004006928,0.002359513,0.0007603609,0.002486432],"category_scores_gemma":[0.0000905363,0.001247252,0.000003213683,0.00007207714,0.0006696525,0.001655617,0.0008653727,0.001851653,0.0001748095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001555113,"about_ca_system_score_gemma":0.0005317701,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07960473,"about_ca_topic_score_gemma":0.0925878,"domain_scores_codex":[0.9944704,0.0003876372,0.00101213,0.001834489,0.001204543,0.00109082],"domain_scores_gemma":[0.9946612,0.0004435637,0.001033775,0.003171841,0.0001266437,0.0005629904],"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":[0.0005045246,0.000208698,0.002090712,0.001538025,0.0008524506,0.0002918921,0.00003219263,0.0002340582,0.0002045459,0.0008111092,0.9913127,0.001919058],"study_design_scores_gemma":[0.001329956,0.0001850904,0.02529489,0.0001214043,0.0003289829,0.0001128299,0.00007543932,0.003057756,0.00007206391,0.000005449787,0.9681783,0.001237826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000949479,0.0009434789,0.0001292185,0.00004239925,0.0008510478,0.001171104,0.9948336,0.000299191,0.0007804139],"genre_scores_gemma":[0.003264319,0.003188141,0.002496823,0.00004994781,0.0009179199,0.00005905027,0.9870284,0.0003761114,0.002619268],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02320418,"threshold_uncertainty_score":0.9989977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019100331346104,"score_gpt":0.2584439989516787,"score_spread":0.2382529956382176,"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."}}