{"id":"W2975600900","doi":"10.26425/2309-3633-2019-2-95-103","title":"Wages in the U.S. Manufacturing industry","year":2019,"lang":"en","type":"article","venue":"UPRAVLENIE / MANAGEMENT (Russia)","topic":"Economic Development and Digital Transformation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies","funders":"","keywords":"Manufacturing; Production (economics); Wage; Distribution (mathematics); Wages and salaries; Labour economics; Economics; Work (physics); Business; Engineering; Marketing; Macroeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006299422,0.0001640702,0.0002240587,0.0003174259,0.00005680569,0.0002092746,0.0004206724,0.0001172558,0.001025381],"category_scores_gemma":[0.000003796091,0.0001533923,0.00007361933,0.0001554149,0.00001983192,0.0006914106,0.00007289396,0.000260407,0.004312223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008424308,"about_ca_system_score_gemma":0.000004829993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002366572,"about_ca_topic_score_gemma":0.000009519996,"domain_scores_codex":[0.9987406,0.00001118482,0.0005365713,0.0003315793,0.00005420478,0.0003258297],"domain_scores_gemma":[0.9994256,0.00002885788,0.000157793,0.0003505932,0.000002990998,0.00003411967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002805179,0.0001665513,0.1619197,0.000256507,0.0001207942,0.00002256795,0.002087381,0.0003422708,0.000001620874,0.8107148,0.005030171,0.01930951],"study_design_scores_gemma":[0.00131866,0.00003451315,0.4917627,0.00005115009,0.00000623347,0.000003278015,0.001183098,0.0004047862,0.0001443576,0.03725796,0.4673611,0.0004722218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4440123,0.00009462834,0.00006640259,0.0009116531,0.0002597864,0.0003372131,0.000007901301,0.00002736156,0.5542828],"genre_scores_gemma":[0.9851038,0.0001581801,0.0002341019,0.0008740079,0.00004140186,0.00004182174,0.0000310195,0.00001640521,0.01349925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7734569,"threshold_uncertainty_score":0.9998878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0214511480902025,"score_gpt":0.1933941438745163,"score_spread":0.1719429957843138,"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."}}