{"id":"W4361276066","doi":"10.1016/j.wear.2023.204852","title":"Surface roughness and morphology evaluation of bearing steel after grinding with multilayer graphene platelets dispersed in different base fluids","year":2023,"lang":"en","type":"article","venue":"Wear","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Universidade Federal de Minas Gerais; Fundação de Apoio à Pesquisa do Distrito Federal; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Department of Mechanical Engineering, University of Alberta; Universidade Federal de Uberlândia; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade de Brasília; Kansas State University","keywords":"Materials science; Grinding; Surface roughness; Lubrication; Lubricant; Scanning electron microscope; Context (archaeology); Tribology; Surface finish; Surface integrity; Base oil; Bearing (navigation); Machining; Composite material; Cutting fluid; Metallurgy; Surface grinding","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001944989,0.00009882755,0.0001327057,0.00009280716,0.00002671456,0.000008964733,0.00003270017,0.00005013272,0.00002222159],"category_scores_gemma":[0.00001979454,0.00008369399,0.0000112373,0.0002195752,0.0000189103,0.0001073951,0.00002436193,0.000101206,0.000001678948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002640152,"about_ca_system_score_gemma":0.000006279417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001577729,"about_ca_topic_score_gemma":0.00005883401,"domain_scores_codex":[0.9994027,0.00002393053,0.0001239535,0.0001399458,0.0001520713,0.000157386],"domain_scores_gemma":[0.9997958,0.00004317482,0.00002054405,0.0000843914,0.00003073123,0.0000253865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004099824,0.00000978653,0.03337423,0.00009630097,0.00001141202,0.000004669984,0.001249547,0.9423714,0.02208503,0.00001379398,0.000001353133,0.000741473],"study_design_scores_gemma":[0.001000344,0.00003164152,0.1717796,0.00009778414,0.00002111771,0.000001561778,0.0002742874,0.8162552,0.01037696,0.00003542514,0.000006425201,0.0001197401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924099,0.0001334835,0.007115667,0.00001333972,0.00006562189,0.0001442432,0.000004326081,0.00007563274,0.00003773486],"genre_scores_gemma":[0.9985338,0.0001084173,0.001269006,0.000002882404,0.000009923611,0.00002059362,0.00001400139,0.00002471389,0.00001668477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1384054,"threshold_uncertainty_score":0.3412942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799517592742296,"score_gpt":0.2521008226949743,"score_spread":0.2341056467675514,"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."}}