{"id":"W2901510825","doi":"10.25071/10315/35212","title":"Multi-Objective Optimization During Machining Ti-6Al-4V Using Nano-Fluids","year":2018,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nano-; Machining; Materials science; Computer science; Metallurgy; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001893756,0.0003033935,0.0002616899,0.0004777845,0.0001582295,0.00008142113,0.0002186196,0.0002022583,0.00005184334],"category_scores_gemma":[0.0001180012,0.0003674903,0.00004624147,0.000664237,0.00003442306,0.0003596288,0.00004773723,0.0003467661,0.000006755427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007794009,"about_ca_system_score_gemma":0.00008612631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001810464,"about_ca_topic_score_gemma":0.006190248,"domain_scores_codex":[0.9982516,0.0000156478,0.0003640832,0.0003661791,0.0001628356,0.0008396942],"domain_scores_gemma":[0.9992536,0.00002620854,0.00003745006,0.0002134485,0.00008190865,0.0003874001],"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":[0.000006653018,0.000008912926,0.0006739714,0.0001059342,0.00002034998,0.0000206641,0.0002223964,0.9960237,0.001251534,0.0002194333,0.000001073455,0.00144534],"study_design_scores_gemma":[0.0004775846,0.00002859682,0.0003341478,0.0002229441,0.00001266991,0.00002288023,0.00002894013,0.9926234,0.005706582,0.00001424463,0.0001147611,0.0004132706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07879682,0.0009697475,0.9181505,0.00002414796,0.0009353012,0.0003399728,0.0000159239,0.0006152192,0.0001522967],"genre_scores_gemma":[0.7503357,0.00004062957,0.2492762,0.0000195573,0.0001665653,0.0000353054,0.00001229203,0.0001066307,0.000007091598],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6715389,"threshold_uncertainty_score":0.9998777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009328373524144895,"score_gpt":0.2436107365202741,"score_spread":0.2342823629961292,"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."}}